Management Engineering

Educational objectives

GENERAL OBJECTIVES
The Financial Markets course aims to provide students with a solid understanding of the modern
financial markets, the main characteristics of different securities, their valuation and risk
management. The course covers fundamental financial concepts such as the time value of money,
risk – return relationship, the term structure of interest rates, and pricing models for bonds, stocks
and derivatives. The course also provides some basic hedging strategies involving financial
derivatives.
�� SPECIFIC OBJECTIVES
 Knowledge and understanding
Students will gain a comprehensive understanding of the core principles of finance,

financial contracts. They will become familiar with the theoretical models used to value
financial instruments and assess the associated risks.
 Applying knowledge and understanding
Students will be able to apply the acquired concepts to evaluate financial assets, analyze
the interest rate term structures, interpret the price movements of stocks and bonds, use
models such as CAPM and APT in portfolio management. They will also be able to use
basic derivative strategies for hedging purposes.
 Making judgements
The course will help students develop critical thinking in evaluating financial decisions and
risk management strategies. They will learn to assess the consistency and appropriateness
of financial tools and strategies in different market conditions.
 Communication skills
Students will be able to clearly communicate technical financial concepts, both in written
and oral form, using appropriate terminology and supporting their arguments with
quantitative data and analytical models.
 Learning skills
The course will foster students’ ability to independently study more advanced financial
topics. They will acquire the foundational knowledge required for further specialization and
continuous learning in the field of finance.

Educational objectives

The course provides students the tools to understand and manage the main
issues relating to corporate finance, investment decision and business
finance. The course is aimed to explain and develop the main methods of
evaluating an investment project and issues related to sources of
financing and optimal financial structure.Risultati di apprendimento attesi (Inglese):
At the end of the course the
student will be able to: a) know the main financial sources, b)
determine the optimal financial structure, c) calculate the cost of
capital, d) select the best project among different alternatives, e)
assess the value of a company using the most common valuation methods.

Educational objectives

OBJECTIVES
The course aims to provide the basic knowledge of Quality in process management for organizations, through the definition and historical evolution of the concept (from Quality Control to Total Quality Management), the regulatory framework, methodologies for analysis and improvement. In particular, the course presents an applicative focus on process mapping and management and on Lean Six Sigma.

EXPECTED LEARNING OUTCOMES
Knowledge and understanding: in-depth knowledge of terminology and reference concepts for quality management, knowledge of the principles of Lean Manufacturing and knowledge of the Lean Six Sigma methodology. Capability: ability to develop analysis and mapping of processes for value generation and identification of waste from a Lean perspective, ability to set up and develop a project according to the requirements of the Lean Six Sigma methodology, through the application of all phases of DMAIC, identifying appropriate performance measurement tools and defining objectives and improvement programs.

Educational objectives

GENERAL OBJECTIVES
The course aims to provide students with knowledge of the various theoretical approaches and the implementation of concrete cases related to policies and tools for human resource management, with a focus on the processes that accompany the employee lifecycle within organizations.

SPECIFIC OBJECTIVES
In-depth knowledge of the processes of Recruitment and Selection, Training, Performance, Potential, and Competency Evaluation, Job Evaluation, Use of assessment tools for employee development and management, and Internal Communication.
The ability to relate HR policies, methods, and tools to industry sectors, organizational processes, and the strategic directions of the company.

KNOWLEDGE AND UNDERSTANDING
The above objectives are framed within the understanding of organizational and relational phenomena involving Human Resources Departments and line Management, who cooperate—each with specific and differentiated responsibilities and skills—for the effective functioning of human resource management and development processes.

APPLICATIVE SKILLS
Ability to analyze organizational phenomena in relation to the human resources variable in different business contexts.
Ability to identify methodological alternatives in HR management processes in relation to contexts and business objectives.

INDEPENDENT JUDGEMENT
The course constantly trains students in the exercise of critical thinking through teaching methodologies that encourage them to express their own judgment on the effectiveness and efficiency of the various implementations of human resource management and development processes.
This training is carried out through the analysis of numerous business cases and testimonials from company managers involved in teaching.

COMMUNICATION SKILLS
Students are expected to develop specific linguistic distinction skills, necessary for a deep understanding of economic, organizational, and psychological topics.
The teaching methodology includes group work focused on analyzing business cases and presenting the results in class.
This approach develops soft skills such as teamwork and public speaking.

LEARNING ABILITY
Students will develop the ability to transform raw information into personal knowledge, elaborated through the analysis of organizational problems and the sharing of ideas and evaluations within the social learning system represented by the classroom group.
The study of the complex relationship between people and organizations will enable students to gain greater awareness and understanding of their own attitudes and motivational approaches in relation to the world of work.

Educational objectives

OBJECTIVES. The course aims to provide the knowledge bases of the Smart Factories in the context of Industry 4.0 which is characterized as the Fourth Industrial Revolution through the analysis of the economic and technological scenario, the identification and classification of intervention key areas, the processes for a strategy 4.0 implementation, the definition of organizational models, the identification of design and management issues.
EXPECTED LEARNING OUTCOMES. Knowledge and understanding: Knowledge of the structural and operating characteristics of Smart Factories within the framework of Industry 4.0. Applying knowledge and understanding: Ability to develop analysis, model problems and identify best techniques for implementing Smart Production in a Smart Factory. The course requires preparation of a technical report (usually prepared in small working groups self-managed by students); the course also aims to promote the development of skills to apply the knowledge acquired through independent learning and teamworking, making independent judgment and communication skills.

Educational objectives

ENG
GENERAL OBJECTIVES
The course aims to provide students with a fundamental understanding of concepts and tools relevant to marketing. Specifically, the course aims to help students understand: the main forces of the marketing environment; the marketing information system; consumer and business buying behaviors; the stages and tools for formulating and implementing a marketing strategy. Furthermore, through the analysis of a series of case studies, the course aims to develop students' critical analysis skills, enabling them to interpret and explain business behavior and outcomes within marketing strategies in light of the concepts learned during the course.

SPECIFIC OBJECTIVES
KNOWLEDGE AND UNDERSTANDING. The course will enable students to acquire knowledge and understanding of the main concepts and fundamental tools of Marketing. Students will learn to recognize and master best practices and success factors of Marketing and apply them in real-world contexts.

APPLICATIVE SKILLS. Thanks to the course, students will be able to formulate a marketing plan, as well as critically evaluate marketing strategies.

JUDGMENT AUTONOMY. The course will empower students to choose, given the main environmental forces, the characteristics of the enterprise and innovation, the best marketing strategies. Additionally, students will develop the ability to critically analyze marketing.

COMMUNICATION SKILLS. By the end of the course, students will be able to explain marketing concepts using internationally established terminology and models, organize information and data in a format and reporting process understandable to professionals.

LEARNING ABILITY. Students will develop independent study skills and critical understanding and evaluation of marketing strategies and related tools.

Educational objectives

GENERAL OBJECTIVES
The course aims to provide students with a basic understanding of concepts and tools relevant to Innovation Management. Specifically, the course aims to help students understand: the forms, models, and sources of innovation; standard conflicts and the definition of dominant design; market entry timing choices; innovation protection mechanisms; the process of developing a new product; the integration of environmental sustainability into marketing strategy and new product development. Furthermore, through the analysis of a series of case studies, the course aims to develop students' critical analysis skills, enabling them to interpret and explain business behavior and outcomes within the context of technological innovation strategies in light of the concepts learned during the course.

SPECIFIC OBJECTIVES
KNOWLEDGE AND UNDERSTANDING. The course will enable students to acquire knowledge and understanding of the main concepts and fundamental tools of Innovation Management. Students will learn to recognize and master best practices and success factors of Innovation Management and apply them in real-world contexts.

APPLICATIVE SKILLS. Thanks to the course, students will be able to critically evaluate an enterprise's technological innovation strategies, as well as classify products based on their environmental impact.

JUDGMENT AUTONOMY. The course will empower students to choose, given the main environmental forces, the characteristics of the enterprise and innovation, the best technological innovation strategies. Additionally, students will develop the ability to critically analyze innovation management.

COMMUNICATION SKILLS. By the end of the course, students will be able to illustrate concepts of innovation management using internationally established terminology and models, organize information and data in a format and reporting process understandable to professionals.

LEARNING ABILITY. Students will develop independent study skills and critical understanding and evaluation of marketing and technological innovation strategies and related tools

Educational objectives

Knowledge and understanding
Students will acquire advanced knowledge of financial market microstructure, algorithmic trading concepts, and the statistical foundations of quantitative strategies. They will thoroughly understand how to translate theoretical trading ideas into computational logic using programming languages such as Python and EasyLanguage.

2) Applying knowledge and understanding
Students will be able to apply theoretical concepts to practically build, test, and optimize automated trading systems. They will use Python for advanced data analysis, statistical modeling, and machine learning integration, and will employ EasyLanguage for rapid strategy prototyping, platform integration, and market execution.

3) Making judgements
Students are encouraged to critically discuss quantitative trading strategies, market anomalies, and algorithmic models in the classroom. The course includes practical sessions in which students apply the acquired knowledge to design, test, and evaluate empirical trading models. Using historical and live market data, students will critically assess the performance and robustness of their strategies implemented through EasyLanguage and Python, learning to distinguish between genuine predictive power and data overfitting.

4) Communication skills
At the end of the course, students will be able to illustrate and explain algorithmic logic, risk metrics (e.g., Sharpe ratio, maximum drawdown), and the strengths and weaknesses of various trading methodologies to a variety of heterogeneous interlocutors (such as investors, risk managers, or software developers). The acquisition of these skills is verified and evaluated not only during the final exam, but also during project presentations and assessments in which, individually or in groups, students are called to present their own trading algorithms and showcase comprehensive backtesting reports.

5) Learning skills
Students acquire the ability to independently research, design, and code quantitative trading systems using Python and EasyLanguage. The tools provided by the course allow for the analysis of complex financial markets, enabling students to build models that simultaneously account for multiple market factors, execution costs, and the strict risk management inherent in algorithmic trading.

Educational objectives

The course aims to describe the founding principles, the scope and the fundamental toolsand
methodologies of project management and of organizational processes.
Starting from the concept of integrated management of projects, all the
main methods for managing the performance variables of quality, time and
cost will be proposed. In line with the main standard processes of
project management the internationally standardized project management
terminology will be used. The course also intends to present the
principles and techniques of business process re-engineering and
management.Risultati di apprendimento attesi (Inglese):At the
end of the course the student will be able to plan a project starting
from the objectives of quality, time and cost defined by internal or
external customers of a company. She/he will also be able to critically
analyze an ongoing project proposing both organizational and managerial
improvements and both the use of correct project management
methodologies.

Educational objectives

The course gives on-field experience of Operations Management, from an operational and technical point of view. The course develops one of the main topics of Operations Management, e.g. company-market relations, supply chain management, production and inventory management.

EXPECTED LEARNING OUTCOMES. Knowledge and understanding: Deepening and consolidation of the knowledge provided in the Operations Management course. Capability: Expected learning outcomes include the ability to develop analyses, model problems, and identify the best techniques for solving major characteristic problems in production management, materials management, logistics management, and supply chain management through project work and on-field experience.

Educational objectives

Main course’s objective is
developing digital competences to make citizens able to use Information
and Communication Technologies (ICT) at work, in day-to-day relationships, in
dealing with public services as well as in culture, entertainment, leisure and
when participating in community and political dialogues. This course contributes
to actualize the EU i2010 program “increasing social
inclusion through reducing digital divide to build a society for
all”. The course promotes ICT use also to put in action the Italian Digital
Administration Low as well as the ICT right use (art.3) and the Public
Administration Data Availability (art 50 capo V).

Educational objectives

The objective is to provide the management engineer with the basis for understanding the theory and principles of innovative optical, photothermal, photoacoustic, radiometric and thermographic techniques and instruments, and to understand the main applications together with their industrial relapses and with a deep analysis of the economical impacts and parameters.

The topics are organized to provide scientific technical expertise in order to work in the field of applied research, making easier the employment of the new engineer in industry.

Contents of the proposed course:

The course will provide the theoretical bases of the optical systems, the laser with its applications. Photothermal, photoacoustic, radiometric, and infrared techniques will be introduced for nonsdestructive evaluation and testing of materials, together with the many applications in different fields: industry, environment, energy, but also biology, medicine, and in the emerging sector of agri-food. Final comparisons will be introduced among the diagnostic techniques by using parameters of the management engeneering. The course contains also some experimental activities in laboratory and the data analysis and processing.

Educational objectives

The course aims at deepening the design, the implementation, the execution and the analysis of the results of
discrete event simulation models. Particular focus will be on the use of specialized software packages and languages
for discrete event simulation and their possible use in connection with optimization software. Students will be
directly involved in laboratory activities and in particular in the implementation of real case studies.

Knowledge and understanding:
Techniques for implementing a discrete event simulation model. Use of software packages dedicated to
discrete event simulation and their possible integration with optimization software.

Applying knowledge and understanding:
To be able to carry out a complete study of a real problem through discrete event simulation, from the preliminary analysis
of the processes, the implementation, up to the interpretation of the results.

Making judgements:
Knowing how to identify the fundamental characteristics of a real process for the construction of a model of
discrete event simulation. Knowing how to evaluate the impact of the output of a simulation on the real case in analysis.

Communication skills:
The laboratory activities will allow the student to learn communicating and sharing simulation models and their outputs.

Learning skills:
The laboratory activities will stimulate the students to the autonomous realization of simulation models, to teamwork,
and to the concrete application of the techniques learned in the course.

Educational objectives

The specific goal of these activities is to enable the students to merge their academic knowledge with professional skills.

Educational objectives

The specific aim is to allow the student to use and expand the bulk of knowledge acquired during the course of study performing some activities in an industrial setting, a company, or a research laboratory.

Educational objectives

This course aims providing knowledges and abilities in the field of Additive Manufacturing with regards to:

- Knowledge about several technologies by means of physical and chemical processes;

- Knowledge about design methods applied to the virtual model and its reparing;

- Ability to manage different processes choosing suitable parameters and fabrication strategies;

- Ability to plan, before the AM fabrication, post processing operation in order to comply with the design requirements;

- Ability to compare additive and traditional processes in order to choose the better one for a specific case;

- Ability to apply the acquired knowledges to case studies;

- Ability to provide a technological transfer.

Educational objectives

GENERAL OBJECTIVES

The course aims to provide students with an integrated understanding of the fundamental tools for
monitoring, improving, and innovating production processes. Specifically, the objective is to
understand the use of Statistical Process Control (SPC), Design of Experiments (DOE), and Life
Cycle Assessment (LCA) as strategic levers for optimizing quality, efficiency, and sustainability.
Through a theoretical-practical approach and the analysis of case studies, the course aims to
develop critical thinking in the combined application of these tools, promoting a systemic and
conscious vision of innovation in manufacturing contexts.

SPECIFIC OBJECTIVES – KNOWLEDGE AND UNDERSTANDING
The course will enable students to acquire the logic and principles governing quality monitoring in
production processes, efficient experiment design for performance optimization, and the systemic
approach to sustainability. Particular attention will be given to understanding technical standards,
analytical methods (e.g., control charts, factorial designs, environmental impact assessments), and
the integration of statistical, experimental, and environmental tools to support informed industrial
decision-making.
APPLICATIVE SKILLS
The theoretical knowledge acquired during the course, applied to real case studies through hands-
on exercises using specialized software (Minitab, openLCA, GaBi), will enable students to design
control charts, conduct DOE analyses, build models to assess environmental impact, and propose
innovative solutions for process optimization.

JUDGMENT AUTONOMY
The course fosters the development of critical thinking through the comparative analysis of
different methodologies, the assessment of their applications, and the discussion of real production
scenarios. Students learn to evaluate data reliability, model robustness, and the appropriateness of
techniques in relation to specific production contexts.

COMMUNICATION SKILLS
At the end of the course, students will be able to effectively communicate the results of statistical,
experimental, and environmental analyses to both technical and non-technical audiences, clearly
explaining their strengths and weaknesses. Communication skills will be exercised during group
discussions and in the technical documentation related to case studies.

LEARNING ABILITY
The course will enhance the ability of students to independently learn new methodologies and tools
in the field of process control and innovation, even in multidisciplinary contexts.

Educational objectives

OBJECTIVES
The course aims to provide the basic knowledge of Quality in process management for organizations, through the definition and historical evolution of the concept (from Quality Control to Total Quality Management), the regulatory framework, methodologies for analysis and improvement. In particular, the course presents an applicative focus on process mapping and management and on Lean Six Sigma.

EXPECTED LEARNING OUTCOMES
Knowledge and understanding: in-depth knowledge of terminology and reference concepts for quality management, knowledge of the principles of Lean Manufacturing and knowledge of the Lean Six Sigma methodology. Capability: ability to develop analysis and mapping of processes for value generation and identification of waste from a Lean perspective, ability to set up and develop a project according to the requirements of the Lean Six Sigma methodology, through the application of all phases of DMAIC, identifying appropriate performance measurement tools and defining objectives and improvement programs.

Educational objectives

To realize the knowledge for design and management of safety and maintenance in industrial complex systems.

Educational objectives

GENERAL OBJECTIVES

The course aims to provide students with an integrated understanding of the fundamental tools for
monitoring, improving, and innovating production processes. Specifically, the objective is to
understand the use of Statistical Process Control (SPC), Design of Experiments (DOE), and Life
Cycle Assessment (LCA) as strategic levers for optimizing quality, efficiency, and sustainability.
Through a theoretical-practical approach and the analysis of case studies, the course aims to
develop critical thinking in the combined application of these tools, promoting a systemic and
conscious vision of innovation in manufacturing contexts.

SPECIFIC OBJECTIVES – KNOWLEDGE AND UNDERSTANDING
The course will enable students to acquire the logic and principles governing quality monitoring in
production processes, efficient experiment design for performance optimization, and the systemic
approach to sustainability. Particular attention will be given to understanding technical standards,
analytical methods (e.g., control charts, factorial designs, environmental impact assessments), and
the integration of statistical, experimental, and environmental tools to support informed industrial
decision-making.
APPLICATIVE SKILLS
The theoretical knowledge acquired during the course, applied to real case studies through hands-
on exercises using specialized software (Minitab, openLCA, GaBi), will enable students to design
control charts, conduct DOE analyses, build models to assess environmental impact, and propose
innovative solutions for process optimization.

JUDGMENT AUTONOMY
The course fosters the development of critical thinking through the comparative analysis of
different methodologies, the assessment of their applications, and the discussion of real production
scenarios. Students learn to evaluate data reliability, model robustness, and the appropriateness of
techniques in relation to specific production contexts.

COMMUNICATION SKILLS
At the end of the course, students will be able to effectively communicate the results of statistical,
experimental, and environmental analyses to both technical and non-technical audiences, clearly
explaining their strengths and weaknesses. Communication skills will be exercised during group
discussions and in the technical documentation related to case studies.

LEARNING ABILITY
The course will enhance the ability of students to independently learn new methodologies and tools
in the field of process control and innovation, even in multidisciplinary contexts.

Educational objectives

This course aims providing knowledges and abilities in the field of Additive Manufacturing with regards to:

- Knowledge about several technologies by means of physical and chemical processes;

- Knowledge about design methods applied to the virtual model and its reparing;

- Ability to manage different processes choosing suitable parameters and fabrication strategies;

- Ability to plan, before the AM fabrication, post processing operation in order to comply with the design requirements;

- Ability to compare additive and traditional processes in order to choose the better one for a specific case;

- Ability to apply the acquired knowledges to case studies;

- Ability to provide a technological transfer.

Educational objectives

The course is aimed at the creation of a knowledge-base in the student on the main processing technologies, typical of metallic materials, in use or expected to be adopted in the manufacturing industry. This course aims also to provide some case studies of typical industrial applications which analyze specific technological aspects and limitations in order to give a technical-practical qualification to the student and solid knowledge of tools that will enable him to understand technological problems, to work by making innovations and structuring its actions in a logical way, developing capacity also strongly requested by companies.Autonomy of judgment: Evaluate and compare the performance expected by the employment of different manufacturing technologies to scenarios which are different from the usual one, elaborating solutions on the basis of known information. Communicative skills: to be able to communicate clearly their own conclusions on issues concerning topics which are objet of the course and on topics concerning the performance of manufacturing processes.

Educational objectives

ENG
GENERAL OBJECTIVES
The course aims to provide students with a fundamental understanding of concepts and tools relevant to marketing. Specifically, the course aims to help students understand: the main forces of the marketing environment; the marketing information system; consumer and business buying behaviors; the stages and tools for formulating and implementing a marketing strategy. Furthermore, through the analysis of a series of case studies, the course aims to develop students' critical analysis skills, enabling them to interpret and explain business behavior and outcomes within marketing strategies in light of the concepts learned during the course.

SPECIFIC OBJECTIVES
KNOWLEDGE AND UNDERSTANDING. The course will enable students to acquire knowledge and understanding of the main concepts and fundamental tools of Marketing. Students will learn to recognize and master best practices and success factors of Marketing and apply them in real-world contexts.

APPLICATIVE SKILLS. Thanks to the course, students will be able to formulate a marketing plan, as well as critically evaluate marketing strategies.

JUDGMENT AUTONOMY. The course will empower students to choose, given the main environmental forces, the characteristics of the enterprise and innovation, the best marketing strategies. Additionally, students will develop the ability to critically analyze marketing.

COMMUNICATION SKILLS. By the end of the course, students will be able to explain marketing concepts using internationally established terminology and models, organize information and data in a format and reporting process understandable to professionals.

LEARNING ABILITY. Students will develop independent study skills and critical understanding and evaluation of marketing strategies and related tools.

Educational objectives

- To address the methodological principles underlying many phases of the life cycle of information systems, with reference not only to technological aspects, but also and above all to those that require attention to the organizational and economic context
- Enrich students' cultural heritage through a series of monographic or seminar contributions

Educational objectives

###################
General Objectives:
###################

Major advances in technology have resulted in the widespread implementation of information systems into businesses and organizations.

This course introduces languages, principles and methods of process modeling, analysis and innovation as critical factors to the
overall success of a business.

The course centers around the role of conceptual (sometimes referred as business) process modeling as a means to understand and capture the
workflows of interest in information systems of various kind. Students will learn the elements of process models and their precise meaning
using the Business Process Model and Notation (BPMN) international standard.

The course will cover processes within organizations (process orchestrations) and also interacting processes involving several organizations
(process choreographies), and will look at techniques to analyze and improve such processes from a formal perspective.

The course will also provide a basic knowledge and understanding of how to design, test and implement information systems for executable processes.

Finally, the course will present methods and tools to properly use process mining techniques, which enable to discover process models (whose structure
is unknown at the outset) starting from the logs recording the concrete events executed by the real workflows.

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Specific Objectives:
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Knowledge and understanding:
----------------------------
At the end of the course, the students:
- learn the main methods to carry out a BPM (Business Process Management) project;
- are able to model a process with the BPMN standard;
- are able to implement and execute a process through a real information system;
- understand process mining algorithms and techniques.

-------------------------------------
Applying knowledge and understanding:
-------------------------------------
The students will be able to use suitable methodological and technological solutions for
(i) modeling a process in BPMN;
(ii) analysing it with quantitative techniques;
(iii) executing and monitoring it with an information system.

------------------
Making judgements:
------------------
The student acquires autonomy of judgment in proposing the most suitable approach to carry out a BPM project.

--------------
Communication:
--------------
The project activities and the lectures of the course allow the students to develop the proper abilities to communicate/share the design choices and
development methods for realizing any step of the business process life-cycle.

-------------------------
Lifelong learning skills:
-------------------------
In addition to the traditional learning skills provided by studying the teaching material, the project activities stimulate the student to
deepen her knowledge of the BPM topic, to improve the teamwork, and to the concrete application of the concepts and techniques investigated
during the course.

Educational objectives

General Objectives.
- Understanding of the main application scenarios relevant to Smart Manufacturing technologies
- Familiarity with the technologies utilized in Smart Manufacturing including those for: (a) Low-level programming of machinery, (b) Data transmission over industrial networks, (c) Development of Artificial Intelligence solutions (Computer Vision, Symbolic Artificial Intelligence, Machine and Deep Learning);
- Ability to design and develop practical solutions in the field of Smart Manufacturing;
- Understanding the integration of smart manufacturing systems into the modern Big Data Continuum;
- Understanding the technological stack used in the industrial field and the necessity of integrating it into higher-level solutions.

Specific objectives:
Ability to:
- Identify the most suitable techniques for developing a Smart Manufacturing solution that meets an industrial need;
- implement the proposed solution, identifying the most appropriate design and implementation tools, among available ones;
- Design and implement experiments to evaluate proposed solutions in realistic settings;

Knowledge and understanding:
- knowledge of main application scenarios;
- knowledge of main techniques of analysis;
- understanding of methodological and theoretical foundations of main analysis techniques;
- knowledge and understanding of main evalutation techniques and corresponding performance indices.

Apply knowledge and understanding:
- Being able to translate application requirements into concrete Smart Manufacturing problems;
- being able to identify the most suitable techniques and tools to address the aforementioned problems;
- being able to estimate in advance, at least qualitatively, the degree of scalability of proposed solutions.

Critical and judgment skills:
Being able to evaluate, also experimentally, the effectiveness and efficiency of proposed solutions.

Communication skills:
Being able to effectively describe the requirements of a problem and provide to third parties the relative specifications, design choices and the reasons underlying these choices.

Learning ability:
The course will facilitate the development of skills for the independent study of topics related to the course. It will also allow students to identify and critically examine material contained in advanced manuals and/or scientific literature, allowing them to face new application scenarios and/or apply alternative techniques to known ones.

Educational objectives

The course provides students the tools to understand and manage the main
issues relating to corporate finance, investment decision and business
finance. The course is aimed to explain and develop the main methods of
evaluating an investment project and issues related to sources of
financing and optimal financial structure.Risultati di apprendimento attesi (Inglese):
At the end of the course the
student will be able to: a) know the main financial sources, b)
determine the optimal financial structure, c) calculate the cost of
capital, d) select the best project among different alternatives, e)
assess the value of a company using the most common valuation methods.

Educational objectives

1. Knowledge and Understanding

On successful completion of the course, students will have demonstrated knowledge and understanding of the foundational principles of sustainable development, circular economy, and Life Cycle Management, including their strategic and tactical implications for operations management and the green governance of the entire supply chain. They will have demonstrated knowledge and understanding of the principles and instruments of corporate environmental governance – including Environmental Management Systems (EMS), statutory reporting requirements, product environmental declarations, and sustainability certifications – as well as of the tools for servitization and digitalization required to guide the transition of traditional production systems towards resilient and eco-efficient product-service models.

2. Applying Knowledge and Understanding

Students will be able to apply their knowledge and understanding of circular economy and Life Cycle Management tools and best practices in real-world industrial contexts, supported by the analysis of case studies. They will be able to apply their understanding of servitization (Product-Service Systems) methods and digital tools to innovate business models and optimize industrial production systems.

3. Making Judgements

Students will have the ability to gather and interpret relevant data in order to critically assess the environmental, economic, and organisational implications of operational decisions along the supply chain, coherently integrating managerial-organisational and technical-operational perspectives. They will be able to form independent judgements on the adequacy of technological and managerial solutions with respect to an organisation’s ecological transition objectives, including reflection on relevant social and ethical dimensions.

4. Communication

Students will be able to communicate information, ideas, problems, and solutions related to industrial sustainability to both specialist and non-specialist audiences, using appropriate technical language. They will be able to draft reports in compliance with environmental reporting and certification standards, as well as to present complex case studies by effectively integrating engineering, managerial, and environmental perspectives.

5. Learning Skills

Students will have developed the learning skills necessary to undertake further study and to independently update their knowledge in response to regulatory, technological, and market developments in the field of industrial sustainability, through critical engagement with scientific literature and industry standards. They will have acquired a mindset of continuous improvement that enables them to transfer the methodological tools developed during the course to new contexts and to integrate emerging approaches with their established knowledge base.

Educational objectives

Knowledge and understanding
Students will acquire advanced knowledge of financial market microstructure, algorithmic trading concepts, and the statistical foundations of quantitative strategies. They will thoroughly understand how to translate theoretical trading ideas into computational logic using programming languages such as Python and EasyLanguage.

2) Applying knowledge and understanding
Students will be able to apply theoretical concepts to practically build, test, and optimize automated trading systems. They will use Python for advanced data analysis, statistical modeling, and machine learning integration, and will employ EasyLanguage for rapid strategy prototyping, platform integration, and market execution.

3) Making judgements
Students are encouraged to critically discuss quantitative trading strategies, market anomalies, and algorithmic models in the classroom. The course includes practical sessions in which students apply the acquired knowledge to design, test, and evaluate empirical trading models. Using historical and live market data, students will critically assess the performance and robustness of their strategies implemented through EasyLanguage and Python, learning to distinguish between genuine predictive power and data overfitting.

4) Communication skills
At the end of the course, students will be able to illustrate and explain algorithmic logic, risk metrics (e.g., Sharpe ratio, maximum drawdown), and the strengths and weaknesses of various trading methodologies to a variety of heterogeneous interlocutors (such as investors, risk managers, or software developers). The acquisition of these skills is verified and evaluated not only during the final exam, but also during project presentations and assessments in which, individually or in groups, students are called to present their own trading algorithms and showcase comprehensive backtesting reports.

5) Learning skills
Students acquire the ability to independently research, design, and code quantitative trading systems using Python and EasyLanguage. The tools provided by the course allow for the analysis of complex financial markets, enabling students to build models that simultaneously account for multiple market factors, execution costs, and the strict risk management inherent in algorithmic trading.

Educational objectives

The course aims to describe the founding principles, the scope and the fundamental toolsand
methodologies of project management and of organizational processes.
Starting from the concept of integrated management of projects, all the
main methods for managing the performance variables of quality, time and
cost will be proposed. In line with the main standard processes of
project management the internationally standardized project management
terminology will be used. The course also intends to present the
principles and techniques of business process re-engineering and
management.Risultati di apprendimento attesi (Inglese):At the
end of the course the student will be able to plan a project starting
from the objectives of quality, time and cost defined by internal or
external customers of a company. She/he will also be able to critically
analyze an ongoing project proposing both organizational and managerial
improvements and both the use of correct project management
methodologies.

Educational objectives

The course gives on-field experience of Operations Management, from an operational and technical point of view. The course develops one of the main topics of Operations Management, e.g. company-market relations, supply chain management, production and inventory management.

EXPECTED LEARNING OUTCOMES. Knowledge and understanding: Deepening and consolidation of the knowledge provided in the Operations Management course. Capability: Expected learning outcomes include the ability to develop analyses, model problems, and identify the best techniques for solving major characteristic problems in production management, materials management, logistics management, and supply chain management through project work and on-field experience.

Educational objectives

The objective is to provide the management engineer with the basis for understanding the theory and principles of innovative optical, photothermal, photoacoustic, radiometric and thermographic techniques and instruments, and to understand the main applications together with their industrial relapses and with a deep analysis of the economical impacts and parameters.

The topics are organized to provide scientific technical expertise in order to work in the field of applied research, making easier the employment of the new engineer in industry.

Contents of the proposed course:

The course will provide the theoretical bases of the optical systems, the laser with its applications. Photothermal, photoacoustic, radiometric, and infrared techniques will be introduced for nonsdestructive evaluation and testing of materials, together with the many applications in different fields: industry, environment, energy, but also biology, medicine, and in the emerging sector of agri-food. Final comparisons will be introduced among the diagnostic techniques by using parameters of the management engeneering. The course contains also some experimental activities in laboratory and the data analysis and processing.

Educational objectives

The course aims at deepening the design, the implementation, the execution and the analysis of the results of
discrete event simulation models. Particular focus will be on the use of specialized software packages and languages
for discrete event simulation and their possible use in connection with optimization software. Students will be
directly involved in laboratory activities and in particular in the implementation of real case studies.

Knowledge and understanding:
Techniques for implementing a discrete event simulation model. Use of software packages dedicated to
discrete event simulation and their possible integration with optimization software.

Applying knowledge and understanding:
To be able to carry out a complete study of a real problem through discrete event simulation, from the preliminary analysis
of the processes, the implementation, up to the interpretation of the results.

Making judgements:
Knowing how to identify the fundamental characteristics of a real process for the construction of a model of
discrete event simulation. Knowing how to evaluate the impact of the output of a simulation on the real case in analysis.

Communication skills:
The laboratory activities will allow the student to learn communicating and sharing simulation models and their outputs.

Learning skills:
The laboratory activities will stimulate the students to the autonomous realization of simulation models, to teamwork,
and to the concrete application of the techniques learned in the course.

Educational objectives

Main course’s objective is
developing digital competences to make citizens able to use Information
and Communication Technologies (ICT) at work, in day-to-day relationships, in
dealing with public services as well as in culture, entertainment, leisure and
when participating in community and political dialogues. This course contributes
to actualize the EU i2010 program “increasing social
inclusion through reducing digital divide to build a society for
all”. The course promotes ICT use also to put in action the Italian Digital
Administration Low as well as the ICT right use (art.3) and the Public
Administration Data Availability (art 50 capo V).

Educational objectives

The specific goal of these activities is to enable the students to merge their academic knowledge with professional skills.

Educational objectives

The specific aim is to allow the student to use and expand the bulk of knowledge acquired during the course of study performing some activities in an industrial setting, a company, or a research laboratory.

Educational objectives

GENERAL OBJECTIVES

The course aims to provide students with an integrated understanding of the fundamental tools for
monitoring, improving, and innovating production processes. Specifically, the objective is to
understand the use of Statistical Process Control (SPC), Design of Experiments (DOE), and Life
Cycle Assessment (LCA) as strategic levers for optimizing quality, efficiency, and sustainability.
Through a theoretical-practical approach and the analysis of case studies, the course aims to
develop critical thinking in the combined application of these tools, promoting a systemic and
conscious vision of innovation in manufacturing contexts.

SPECIFIC OBJECTIVES – KNOWLEDGE AND UNDERSTANDING
The course will enable students to acquire the logic and principles governing quality monitoring in
production processes, efficient experiment design for performance optimization, and the systemic
approach to sustainability. Particular attention will be given to understanding technical standards,
analytical methods (e.g., control charts, factorial designs, environmental impact assessments), and
the integration of statistical, experimental, and environmental tools to support informed industrial
decision-making.
APPLICATIVE SKILLS
The theoretical knowledge acquired during the course, applied to real case studies through hands-
on exercises using specialized software (Minitab, openLCA, GaBi), will enable students to design
control charts, conduct DOE analyses, build models to assess environmental impact, and propose
innovative solutions for process optimization.

JUDGMENT AUTONOMY
The course fosters the development of critical thinking through the comparative analysis of
different methodologies, the assessment of their applications, and the discussion of real production
scenarios. Students learn to evaluate data reliability, model robustness, and the appropriateness of
techniques in relation to specific production contexts.

COMMUNICATION SKILLS
At the end of the course, students will be able to effectively communicate the results of statistical,
experimental, and environmental analyses to both technical and non-technical audiences, clearly
explaining their strengths and weaknesses. Communication skills will be exercised during group
discussions and in the technical documentation related to case studies.

LEARNING ABILITY
The course will enhance the ability of students to independently learn new methodologies and tools
in the field of process control and innovation, even in multidisciplinary contexts.

Educational objectives

This course aims providing knowledges and abilities in the field of Additive Manufacturing with regards to:

- Knowledge about several technologies by means of physical and chemical processes;

- Knowledge about design methods applied to the virtual model and its reparing;

- Ability to manage different processes choosing suitable parameters and fabrication strategies;

- Ability to plan, before the AM fabrication, post processing operation in order to comply with the design requirements;

- Ability to compare additive and traditional processes in order to choose the better one for a specific case;

- Ability to apply the acquired knowledges to case studies;

- Ability to provide a technological transfer.

Educational objectives

OBJECTIVES. The course aims to provide the knowledge bases of the Smart Factories in the context of Industry 4.0 which is characterized as the Fourth Industrial Revolution through the analysis of the economic and technological scenario, the identification and classification of intervention key areas, the processes for a strategy 4.0 implementation, the definition of organizational models, the identification of design and management issues.
EXPECTED LEARNING OUTCOMES. Knowledge and understanding: Knowledge of the structural and operating characteristics of Smart Factories within the framework of Industry 4.0. Applying knowledge and understanding: Ability to develop analysis, model problems and identify best techniques for implementing Smart Production in a Smart Factory. The course requires preparation of a technical report (usually prepared in small working groups self-managed by students); the course also aims to promote the development of skills to apply the knowledge acquired through independent learning and teamworking, making independent judgment and communication skills.

Educational objectives

ENG
GENERAL OBJECTIVES
The course aims to provide students with a fundamental understanding of concepts and tools relevant to marketing. Specifically, the course aims to help students understand: the main forces of the marketing environment; the marketing information system; consumer and business buying behaviors; the stages and tools for formulating and implementing a marketing strategy. Furthermore, through the analysis of a series of case studies, the course aims to develop students' critical analysis skills, enabling them to interpret and explain business behavior and outcomes within marketing strategies in light of the concepts learned during the course.

SPECIFIC OBJECTIVES
KNOWLEDGE AND UNDERSTANDING. The course will enable students to acquire knowledge and understanding of the main concepts and fundamental tools of Marketing. Students will learn to recognize and master best practices and success factors of Marketing and apply them in real-world contexts.

APPLICATIVE SKILLS. Thanks to the course, students will be able to formulate a marketing plan, as well as critically evaluate marketing strategies.

JUDGMENT AUTONOMY. The course will empower students to choose, given the main environmental forces, the characteristics of the enterprise and innovation, the best marketing strategies. Additionally, students will develop the ability to critically analyze marketing.

COMMUNICATION SKILLS. By the end of the course, students will be able to explain marketing concepts using internationally established terminology and models, organize information and data in a format and reporting process understandable to professionals.

LEARNING ABILITY. Students will develop independent study skills and critical understanding and evaluation of marketing strategies and related tools.

Educational objectives

The purpose of this course is to study mathematical models useful in taking decisions when more than a decision maker is present.
In particular variational inequalities and game theoretic models will be analyzed in detail. Emphasis will be given to computational aspects.
At the end of the course the student will be able to cast equilibrium problems as variational inequalities or by using game models and to develop suitable solution algorithms. He/She will also learn how to use game theoretic models and how to find algorithmically their equilibria and in realistic cases.

Knowledge and understanding
The aim of the course is to give students a basic knowledge of analytic methods for multi-agent environments analysis.
Students will be able to model mathematically complex interactions among (economic) agents and to analyze the consequences of different choices.

Applying knowledge and understanding
By the end of the course students will be able apply analytical tools to the analysis of economic and industrial settings.

Making judgements
Lectures and practical exercises will provide students with the ability toassess the main strengths and weaknesses of the learned theoretical models.
Students will also be able to gauge consequences of different decisionswhen using multi agent models.

Communication
By the end of the course, students will be able to discuss relevant information, ideas, problems and solutions both with a specialized and a non-specialized audience.

Lifelong learning skills
Students are expected to develop those learning skills necessary to undertake additional studieson the relevant topics with a high degree of autonomy. During the course, students are encouraged to investigate further topics of major interest by consulting supplementary academic publications, specialized books, and internet sites.

Educational objectives

GENERAL OBJECTIVES
The course aims to provide students with a basic understanding of concepts and tools relevant to Innovation Management. Specifically, the course aims to help students understand: the forms, models, and sources of innovation; standard conflicts and the definition of dominant design; market entry timing choices; innovation protection mechanisms; the process of developing a new product; the integration of environmental sustainability into marketing strategy and new product development. Furthermore, through the analysis of a series of case studies, the course aims to develop students' critical analysis skills, enabling them to interpret and explain business behavior and outcomes within the context of technological innovation strategies in light of the concepts learned during the course.

SPECIFIC OBJECTIVES
KNOWLEDGE AND UNDERSTANDING. The course will enable students to acquire knowledge and understanding of the main concepts and fundamental tools of Innovation Management. Students will learn to recognize and master best practices and success factors of Innovation Management and apply them in real-world contexts.

APPLICATIVE SKILLS. Thanks to the course, students will be able to critically evaluate an enterprise's technological innovation strategies, as well as classify products based on their environmental impact.

JUDGMENT AUTONOMY. The course will empower students to choose, given the main environmental forces, the characteristics of the enterprise and innovation, the best technological innovation strategies. Additionally, students will develop the ability to critically analyze innovation management.

COMMUNICATION SKILLS. By the end of the course, students will be able to illustrate concepts of innovation management using internationally established terminology and models, organize information and data in a format and reporting process understandable to professionals.

LEARNING ABILITY. Students will develop independent study skills and critical understanding and evaluation of marketing and technological innovation strategies and related tools

Educational objectives

Knowledge and understanding
The course introduces students to the economics and management of networks. On the one hand, the course illustrates the main features of the new information economy, and discusses the prevailing and emerging business models. On the other hand, it explores competition and regulation issues in liberalized network industries, such as telecommunications, energy, and transportation.

Applying knowledge and understanding
Students are expected to be able to use methods and models of microeconomics and industrial organization to understand and analyze the impact of technology and demand on market structure, firms’ strategies and business models in the new information economy. They will also gain insight on the rationale and the scope for public policy in network industries.

Making judgements
Lectures, practical exercises and problem-solving sessions will provide students with the ability to assess the main strengths and weaknesses of theoretical models when used to explain empirical evidence and case studies in the new information economy and in network industries.

Communication
By the end of the course, students are able to point out the main features of the new information economy and network industries, and to discuss relevant information, ideas, problems and solutions both with a specialized and a non-specialized audience. These capabilities are tested and evaluated in the final written exam and possibly in the oral exam as well as in the project work.

Lifelong learning skills
Students are expected to develop those learning skills necessary to undertake additional studies on relevant topics in the field of the new information economy and network industries with a high degree of autonomy. During the course, students are encouraged to investigate further any topics of major interest, by consulting supplementary academic publications, specialized books, and internet sites. These capabilities are tested and evaluated in the final written exam and possibly in the oral exam as well as in the project work, where students may have to discuss and solve some new problems based on the topics and material covered in class.

Educational objectives

ENG
GENERAL OBJECTIVES
The course aims to provide students with a fundamental understanding of concepts and tools relevant to marketing. Specifically, the course aims to help students understand: the main forces of the marketing environment; the marketing information system; consumer and business buying behaviors; the stages and tools for formulating and implementing a marketing strategy. Furthermore, through the analysis of a series of case studies, the course aims to develop students' critical analysis skills, enabling them to interpret and explain business behavior and outcomes within marketing strategies in light of the concepts learned during the course.

SPECIFIC OBJECTIVES
KNOWLEDGE AND UNDERSTANDING. The course will enable students to acquire knowledge and understanding of the main concepts and fundamental tools of Marketing. Students will learn to recognize and master best practices and success factors of Marketing and apply them in real-world contexts.

APPLICATIVE SKILLS. Thanks to the course, students will be able to formulate a marketing plan, as well as critically evaluate marketing strategies.

JUDGMENT AUTONOMY. The course will empower students to choose, given the main environmental forces, the characteristics of the enterprise and innovation, the best marketing strategies. Additionally, students will develop the ability to critically analyze marketing.

COMMUNICATION SKILLS. By the end of the course, students will be able to explain marketing concepts using internationally established terminology and models, organize information and data in a format and reporting process understandable to professionals.

LEARNING ABILITY. Students will develop independent study skills and critical understanding and evaluation of marketing strategies and related tools.

Educational objectives

The course introduces to the nonlinear continuous optimization problems and describes the main methodological approaches to efficiently solve this class of optimization problems.

At the end of the course, the student should be able to identify the most suitable methodological approach to tackle the real problem to be solved [A) knowledge and understanding]. The student should also be able to correctly use the software available in literature and/or to realize new codes for solving nonlinear continuous optimization problems [B) applying knowledge and understanding]. The student should also have the ability to integrate the mathematical tools proposed in the course to tackle complex real problems [ C) making judgements]. The student should also show that he / she is able to communicate and transfer the methodological approaches used in the considered problems [ D) communication skills]. Finally, the student should have learned well the concepts and methodologies proposed in the course that allow him / her to independently deep further methodological developments of Continuous Optimization [ E) learning skills].
The skills achieved by the student will be verified by a written exam.

Educational objectives

OBJECTIVES
The course aims to provide the basic knowledge of Quality in process management for organizations, through the definition and historical evolution of the concept (from Quality Control to Total Quality Management), the regulatory framework, methodologies for analysis and improvement. In particular, the course presents an applicative focus on process mapping and management and on Lean Six Sigma.

EXPECTED LEARNING OUTCOMES
Knowledge and understanding: in-depth knowledge of terminology and reference concepts for quality management, knowledge of the principles of Lean Manufacturing and knowledge of the Lean Six Sigma methodology. Capability: ability to develop analysis and mapping of processes for value generation and identification of waste from a Lean perspective, ability to set up and develop a project according to the requirements of the Lean Six Sigma methodology, through the application of all phases of DMAIC, identifying appropriate performance measurement tools and defining objectives and improvement programs.

Educational objectives

GENERAL OBJECTIVES
The course aims to provide students with a basic understanding of concepts and tools relevant to Innovation Management. Specifically, the course aims to help students understand: the forms, models, and sources of innovation; standard conflicts and the definition of dominant design; market entry timing choices; innovation protection mechanisms; the process of developing a new product; the integration of environmental sustainability into marketing strategy and new product development. Furthermore, through the analysis of a series of case studies, the course aims to develop students' critical analysis skills, enabling them to interpret and explain business behavior and outcomes within the context of technological innovation strategies in light of the concepts learned during the course.

SPECIFIC OBJECTIVES
KNOWLEDGE AND UNDERSTANDING. The course will enable students to acquire knowledge and understanding of the main concepts and fundamental tools of Innovation Management. Students will learn to recognize and master best practices and success factors of Innovation Management and apply them in real-world contexts.

APPLICATIVE SKILLS. Thanks to the course, students will be able to critically evaluate an enterprise's technological innovation strategies, as well as classify products based on their environmental impact.

JUDGMENT AUTONOMY. The course will empower students to choose, given the main environmental forces, the characteristics of the enterprise and innovation, the best technological innovation strategies. Additionally, students will develop the ability to critically analyze innovation management.

COMMUNICATION SKILLS. By the end of the course, students will be able to illustrate concepts of innovation management using internationally established terminology and models, organize information and data in a format and reporting process understandable to professionals.

LEARNING ABILITY. Students will develop independent study skills and critical understanding and evaluation of marketing and technological innovation strategies and related tools

Educational objectives

GENERAL OBJECTIVES
The course aims to provide students with an in-depth understanding of the strategic and operational aspects of corporate purchasing management.
Through the analysis of purchasing strategies, spending analysis techniques, and purchasing portfolio matrices, the course seeks to develop the ability to effectively plan and manage the procurement process.
Particular attention will be devoted to the study of operational activities related to procurement, such as supplier qualification, the design of competitive tender procedures, quantitative analyses to support negotiations, the development of cost and remuneration models, and vendor rating systems.
At the end of the course, students will be able to apply tools and methodologies to develop purchasing strategies, select reliable and efficient suppliers, and define contractual agreements consistent with corporate needs.

SPECIFIC OBJECTIVES
Knowledge and understanding
Students will acquire:
-knowledge of the main sourcing strategies and purchasing portfolio matrices;
-understanding of procurement processes and operational phases, including supplier qualification, tender management, cost model development, and vendor rating.

Applying knowledge and understanding
Students will be able to:
-analyze corporate spending and classify product groups;
-develop purchasing strategies tailored to specific supply segments;
-select suppliers using quantitative and qualitative tools;
-design competitive tender procedures;
-support the negotiation process and contract design through structured analyses.

Making judgements
The course will develop the students’ ability to:
-critically evaluate sourcing strategies based on market and organizational characteristics;
-independently select analytical methods and operational tools for procurement management.

Communication skills
At the end of the course, students will be able to:
-clearly and systematically present procurement strategies and decisions;
-draft technical documents and analysis reports using specialized terminology and internationally recognized models.

Learning skills
Students will develop autonomous learning abilities, enabling them to:
-deepen their knowledge of advanced purchasing management methodologies;
-stay updated on the evolution of procurement practices and tools in dynamic and complex environments.

Educational objectives

GENERAL OBJECTIVES
The course aims to provide students with knowledge of the various theoretical approaches and the implementation of concrete cases related to policies and tools for human resource management, with a focus on the processes that accompany the employee lifecycle within organizations.

SPECIFIC OBJECTIVES
In-depth knowledge of the processes of Recruitment and Selection, Training, Performance, Potential, and Competency Evaluation, Job Evaluation, Use of assessment tools for employee development and management, and Internal Communication.
The ability to relate HR policies, methods, and tools to industry sectors, organizational processes, and the strategic directions of the company.

KNOWLEDGE AND UNDERSTANDING
The above objectives are framed within the understanding of organizational and relational phenomena involving Human Resources Departments and line Management, who cooperate—each with specific and differentiated responsibilities and skills—for the effective functioning of human resource management and development processes.

APPLICATIVE SKILLS
Ability to analyze organizational phenomena in relation to the human resources variable in different business contexts.
Ability to identify methodological alternatives in HR management processes in relation to contexts and business objectives.

INDEPENDENT JUDGEMENT
The course constantly trains students in the exercise of critical thinking through teaching methodologies that encourage them to express their own judgment on the effectiveness and efficiency of the various implementations of human resource management and development processes.
This training is carried out through the analysis of numerous business cases and testimonials from company managers involved in teaching.

COMMUNICATION SKILLS
Students are expected to develop specific linguistic distinction skills, necessary for a deep understanding of economic, organizational, and psychological topics.
The teaching methodology includes group work focused on analyzing business cases and presenting the results in class.
This approach develops soft skills such as teamwork and public speaking.

LEARNING ABILITY
Students will develop the ability to transform raw information into personal knowledge, elaborated through the analysis of organizational problems and the sharing of ideas and evaluations within the social learning system represented by the classroom group.
The study of the complex relationship between people and organizations will enable students to gain greater awareness and understanding of their own attitudes and motivational approaches in relation to the world of work.

Educational objectives

General outcomes:

The course will present a broad survey of topics at the interface of computer
science, data science, and economics, emphasizing efficiency, robustness, and application to emerging online markets. It will introduce the principles of algorithmic game theory and mechanism design, algorithmic market design, as well as machine learning in games and markets. It will demonstrate applications to case studies in Web search and advertising, network economics, Data, cryptocurrency, and AI markets.

Specific outcomes:
Knowledge and understanding:
The algorithmic and mathematical economics principles underlying the design and the operation of efficient and robust online markets. The application of these principles in concrete examples of online markets.

Applying knowledge and understanding:
Being able to design and analyze algorithms for concrete online market applications with respect to the requirements of efficiency and robustness.

Making judgements:
Being able to evaluate the quality of an algorithm for online market applications, discriminating the modeling aspects from those related to algorithmic and system implementation.

Communication skills:
Ability to communicate and share the modeling choices and system requirements, as well as the results of the analysis of the efficiency of online market algorithms.

Learning skills:
The course stimulates the students to acquire learning skills at the crossroads of computer science, economics, and digital market applications, including the different languages used in these fields.

Educational objectives

1) Knowledge and understanding
During the lessons of Data-driven Economics, students acquire the basic theoretical elements of econometric analysis. Theoretical lessons are aimed at guiding students in the acquisition of the basics of simple and multiple regression models, starting from the relative assumptions, and then proceeding with the estimation and inference procedures. The course contents cover both the estimation of linear and non-linear models and the analysis of both cross-sectional and longitudinal data.
2) Applicate applying knowledge and understanding
The students of the Data-driven Economics course are able to apply the notions acquired during the theoretical lessons to a wide range of problems of an empirical nature. They acquire the ability to build econometric models aimed at giving empirical content to economic relations and are also able to establish a causal link between two or more variables in the economic field.
3) Making judgements
Students are encouraged to critically discuss empirical studies published in the economic/managerial field in the classroom. The Data-driven Economics course also includes a laboratory in which students apply the acquired knowledge of econometrics to the estimation of empirical models carried out using data made available by the teacher.
4) Communication skills
At the end of the course, students are able to illustrate and explain the strengths and weaknesses of a wide range of empirical methodologies to a variety of heterogeneous interlocutors in terms of training and professional role. The acquisition of these skills is verified and evaluated not only during the final exam, by means of a written test and a possible oral test, but also during flipped class sessions in which, individually or in groups, students are called to present empirical studies published in the economic/managerial field.
5) Learning skills
Students acquire the ability to independently conduct empirical analyses by building econometric models to be estimated using data with diversified structures. The tools provided by the course allow for the analysis of systems in which a large number of factors simultaneously contribute to explaining their states and impact assessments that take into account the uncertainty and risk inherent in the application of policies. The acquisition of these skills is verified and evaluated on during the final exam, by means a written test and a possible oral test, in which the student can be called to discuss empirical problems on the basis of the topics covered and the reference material distributed during the course.

Educational objectives

GENERAL OBJECTIVES

The course aims to give students the basic tools to understand the strategic role that the green economy and bioeconomy will have in the transition of economic systems towards the goals of sustainability in order to provide a proper balance of ecosystems. The production, storage and sustainable distribution of energy are a goal to be pursued towards the decarbonisation of energy systems. To this end, it will be essential to provide knowledge about the mechanisms of operation and regulation of the energy value chain. The complexity of the issues addressed requires an interdisciplinary approach in which economic and managerial analysis will play a key role.

SPECIFIC OBJECTIVES

Knowledge and understanding
The program aims to provide the basic skills of models and methodologies suitable to extend and integrate the understanding of the interfaces and interaction between ecosystems and the economy with a particular focus on the energy sector including regulatory systems.

Applying knowledge and understanding
The student will have gained the basic knowledge necessary to understand the issues of resource management from a sustainable perspective and will be able to contribute both within research centres, institutions and international organizations, as well as in the private sector that shows a growing attention to management issues that can propose solutions to climate change. The student will be able to frame and plan investments in policy scenarios ensuring a sustainable business in the long term.

Making judgements
The course aims to provide a dual training both theoretical, such as to make the student able to evaluate data and information obtained through databases and scientific literature of reference and managerial, such as to apply the methodologies to real case studies. The economic-financial tools of the energy business will allow defining the optimal use of resources also considering the regulatory aspects of the energy sector.

Communication skills
The student will be able to expose new concepts of economics and management of energy sources and services knowing the specific terminology of reference. Based on the concepts and models presented in the course she/he will expand her/his view on sustainable development models and understand how the public and private sectors economically evaluate green economy and bioeconomy models. The student will be able to provide synthesis results that consider the different perspectives of analysis.

Educational objectives

GENERAL OBJECTIVES OF THE COURSE:
The course, with an interdisciplinary approach, combines theoretical lectures on the economics of production, with lectures on the main econometric approaches proposed in the literature, including recent developments, and practical sessions to introduce to the main open source software available to carry out productivity and efficiency analysis.
The main objectives of the course are:
- Present a general overview on the economic theory of productivity and efficiency;
- Propose a unified framework on the main methodologies available to estimate and compare productivity and efficiency of Decision Making Units (DMUs);
- Make an introduction to the main open source software available to estimate productivity and efficiency;
- Provide laboratory sessions to implement productivity and efficiency analyses in practice;
- Provide the basic concepts to analyse the specialised literature;
- Interact with students through assisted laboratory and the realization of a practical work on real data, seminars and oral presentations.

SPECIFIC OBJECTIVES:
• knowledge and understanding: demonstrate the knowledge of the basic elements of productivity and efficiency analysis;
• ability to apply knowledge and understanding: to be able to apply efficiency analysis techniques learned during the course in its own engineering area of specialization;
• judgment autonomy: to be able to perform an efficiency analysis with critical spirit, choosing the appropriate method and correctly implementing it.
• communication skills: being able to communicate the results of the analysis and its information to different types of interlocutors;
• learning skills: to develop the necessary skills to apply and develop autonomously the methods learned during the course.

Educational objectives

General objectives

The course is aimed at providing students with the basic knowledge concerning the complex relationships between the economic system and the environment. To this aim, the course is aimed at discussing with students that the current linear economic model “take-make-dispose” is no more sustainable, given the strong environmental constraints that our society must tackle, as well as the main policy and business strategies aimed at favoring the transition towards the circular economic model, able to respect the above-mentioned environmental constraints.
The main topics addressed during the class are the following: theories and evidences related to climate change; relationships between economic system and the environment; international agreements and policy measures related to the environment; production-based and consumption-based environmental accounting, with particular focus on climate and energy; principles of circular economy; industrial ecology and industrial symbiosis; sustainable supply chain management.
Through the analysis of case studies, the course is aimed to provide students with the critical ability to interpret the companies’ behavior towards the circular economy, design new sustainable and circular business models, and reconvert the current business models according to the principles of circular economy.

Specific objectives

Knowledge and understanding: The course allows students to know the main managerial strategies supporting the company’s transition towards the circular economy.

Apply knowledge and understanding: Students will be able to design circular business models and sustainable business models, also thanks to the experience of project work. At the end of the course, students will be able to assess how much a given business model is sustainable.

Critical and judgment skills: Students will be able to assess ex ante the implications – from the economic, social, and environmental perspective – of a given circular business model.

Communication skills: At the end of the course, students will be able to discuss the topics addressed during the class by using the consolidated terminology, both from the scientific and the managerial perspective.

Learning ability: Students will develop the autonomous ability to study and critically assess the business choices of companies. The use of business games for some topics of the class will allow students to learn by doing.

Educational objectives

Knowledge and understanding
Students will acquire advanced knowledge of financial market microstructure, algorithmic trading concepts, and the statistical foundations of quantitative strategies. They will thoroughly understand how to translate theoretical trading ideas into computational logic using programming languages such as Python and EasyLanguage.

2) Applying knowledge and understanding
Students will be able to apply theoretical concepts to practically build, test, and optimize automated trading systems. They will use Python for advanced data analysis, statistical modeling, and machine learning integration, and will employ EasyLanguage for rapid strategy prototyping, platform integration, and market execution.

3) Making judgements
Students are encouraged to critically discuss quantitative trading strategies, market anomalies, and algorithmic models in the classroom. The course includes practical sessions in which students apply the acquired knowledge to design, test, and evaluate empirical trading models. Using historical and live market data, students will critically assess the performance and robustness of their strategies implemented through EasyLanguage and Python, learning to distinguish between genuine predictive power and data overfitting.

4) Communication skills
At the end of the course, students will be able to illustrate and explain algorithmic logic, risk metrics (e.g., Sharpe ratio, maximum drawdown), and the strengths and weaknesses of various trading methodologies to a variety of heterogeneous interlocutors (such as investors, risk managers, or software developers). The acquisition of these skills is verified and evaluated not only during the final exam, but also during project presentations and assessments in which, individually or in groups, students are called to present their own trading algorithms and showcase comprehensive backtesting reports.

5) Learning skills
Students acquire the ability to independently research, design, and code quantitative trading systems using Python and EasyLanguage. The tools provided by the course allow for the analysis of complex financial markets, enabling students to build models that simultaneously account for multiple market factors, execution costs, and the strict risk management inherent in algorithmic trading.

Educational objectives

The course aims to describe the founding principles, the scope and the fundamental toolsand
methodologies of project management and of organizational processes.
Starting from the concept of integrated management of projects, all the
main methods for managing the performance variables of quality, time and
cost will be proposed. In line with the main standard processes of
project management the internationally standardized project management
terminology will be used. The course also intends to present the
principles and techniques of business process re-engineering and
management.Risultati di apprendimento attesi (Inglese):At the
end of the course the student will be able to plan a project starting
from the objectives of quality, time and cost defined by internal or
external customers of a company. She/he will also be able to critically
analyze an ongoing project proposing both organizational and managerial
improvements and both the use of correct project management
methodologies.

Educational objectives

The course gives on-field experience of Operations Management, from an operational and technical point of view. The course develops one of the main topics of Operations Management, e.g. company-market relations, supply chain management, production and inventory management.

EXPECTED LEARNING OUTCOMES. Knowledge and understanding: Deepening and consolidation of the knowledge provided in the Operations Management course. Capability: Expected learning outcomes include the ability to develop analyses, model problems, and identify the best techniques for solving major characteristic problems in production management, materials management, logistics management, and supply chain management through project work and on-field experience.

Educational objectives

The objective is to provide the management engineer with the basis for understanding the theory and principles of innovative optical, photothermal, photoacoustic, radiometric and thermographic techniques and instruments, and to understand the main applications together with their industrial relapses and with a deep analysis of the economical impacts and parameters.

The topics are organized to provide scientific technical expertise in order to work in the field of applied research, making easier the employment of the new engineer in industry.

Contents of the proposed course:

The course will provide the theoretical bases of the optical systems, the laser with its applications. Photothermal, photoacoustic, radiometric, and infrared techniques will be introduced for nonsdestructive evaluation and testing of materials, together with the many applications in different fields: industry, environment, energy, but also biology, medicine, and in the emerging sector of agri-food. Final comparisons will be introduced among the diagnostic techniques by using parameters of the management engeneering. The course contains also some experimental activities in laboratory and the data analysis and processing.

Educational objectives

The course aims at deepening the design, the implementation, the execution and the analysis of the results of
discrete event simulation models. Particular focus will be on the use of specialized software packages and languages
for discrete event simulation and their possible use in connection with optimization software. Students will be
directly involved in laboratory activities and in particular in the implementation of real case studies.

Knowledge and understanding:
Techniques for implementing a discrete event simulation model. Use of software packages dedicated to
discrete event simulation and their possible integration with optimization software.

Applying knowledge and understanding:
To be able to carry out a complete study of a real problem through discrete event simulation, from the preliminary analysis
of the processes, the implementation, up to the interpretation of the results.

Making judgements:
Knowing how to identify the fundamental characteristics of a real process for the construction of a model of
discrete event simulation. Knowing how to evaluate the impact of the output of a simulation on the real case in analysis.

Communication skills:
The laboratory activities will allow the student to learn communicating and sharing simulation models and their outputs.

Learning skills:
The laboratory activities will stimulate the students to the autonomous realization of simulation models, to teamwork,
and to the concrete application of the techniques learned in the course.

Educational objectives

Main course’s objective is
developing digital competences to make citizens able to use Information
and Communication Technologies (ICT) at work, in day-to-day relationships, in
dealing with public services as well as in culture, entertainment, leisure and
when participating in community and political dialogues. This course contributes
to actualize the EU i2010 program “increasing social
inclusion through reducing digital divide to build a society for
all”. The course promotes ICT use also to put in action the Italian Digital
Administration Low as well as the ICT right use (art.3) and the Public
Administration Data Availability (art 50 capo V).

Educational objectives

The specific goal of these activities is to enable the students to merge their academic knowledge with professional skills.

Educational objectives

The specific aim is to allow the student to use and expand the bulk of knowledge acquired during the course of study performing some activities in an industrial setting, a company, or a research laboratory.

Educational objectives

GENERAL OBJECTIVES

The course aims to provide students with an integrated understanding of the fundamental tools for
monitoring, improving, and innovating production processes. Specifically, the objective is to
understand the use of Statistical Process Control (SPC), Design of Experiments (DOE), and Life
Cycle Assessment (LCA) as strategic levers for optimizing quality, efficiency, and sustainability.
Through a theoretical-practical approach and the analysis of case studies, the course aims to
develop critical thinking in the combined application of these tools, promoting a systemic and
conscious vision of innovation in manufacturing contexts.

SPECIFIC OBJECTIVES – KNOWLEDGE AND UNDERSTANDING
The course will enable students to acquire the logic and principles governing quality monitoring in
production processes, efficient experiment design for performance optimization, and the systemic
approach to sustainability. Particular attention will be given to understanding technical standards,
analytical methods (e.g., control charts, factorial designs, environmental impact assessments), and
the integration of statistical, experimental, and environmental tools to support informed industrial
decision-making.
APPLICATIVE SKILLS
The theoretical knowledge acquired during the course, applied to real case studies through hands-
on exercises using specialized software (Minitab, openLCA, GaBi), will enable students to design
control charts, conduct DOE analyses, build models to assess environmental impact, and propose
innovative solutions for process optimization.

JUDGMENT AUTONOMY
The course fosters the development of critical thinking through the comparative analysis of
different methodologies, the assessment of their applications, and the discussion of real production
scenarios. Students learn to evaluate data reliability, model robustness, and the appropriateness of
techniques in relation to specific production contexts.

COMMUNICATION SKILLS
At the end of the course, students will be able to effectively communicate the results of statistical,
experimental, and environmental analyses to both technical and non-technical audiences, clearly
explaining their strengths and weaknesses. Communication skills will be exercised during group
discussions and in the technical documentation related to case studies.

LEARNING ABILITY
The course will enhance the ability of students to independently learn new methodologies and tools
in the field of process control and innovation, even in multidisciplinary contexts.

Educational objectives

This course aims providing knowledges and abilities in the field of Additive Manufacturing with regards to:

- Knowledge about several technologies by means of physical and chemical processes;

- Knowledge about design methods applied to the virtual model and its reparing;

- Ability to manage different processes choosing suitable parameters and fabrication strategies;

- Ability to plan, before the AM fabrication, post processing operation in order to comply with the design requirements;

- Ability to compare additive and traditional processes in order to choose the better one for a specific case;

- Ability to apply the acquired knowledges to case studies;

- Ability to provide a technological transfer.

Educational objectives

The course aims at illustrating the analytical foundations of the study of the Queueing systems,
which are structures characterized by random arrival of clients who request a service to be performed by a server.
The purpose is to evaluate some fundamental Key Performance Indicators in order to efficiently design the system.
The course also aims at providing the basic tools for building and using discrete event simulation models. Discrete event
simulation techniques are introduced as a tool to reproduce real processes - already existing or to be designed - through
appropriate models, in order to study the effects of possible interventions, in the first case, or to evaluate different
design and/or strategic choices in the second case.

Knowledge and understanding:
Queueing systems models and their main Key Performance Indicators. Building and validating discrete event simulation
models. Designing of experiments and executing of simulation runs.

Applying knowledge and understanding:
To be able to build and to solve queueing models. To be able to design, to execute and to assess the output of a discrete
event simulation.

Making judgements:
To be able to recognize real problems which can be modelled as queueing systems. Knowing how to identify the characteristic
elements of a real process for the construction of a simulation model. Knowing how to analyze the output of a
discrete event simulation.

Communication skills:
Course lectures and exercises should provide the student with communication and sharing skill concerning the modelling by
means of queueing systems and discrete event simulation models of real problems.

Learning skills:
In addition to the classic learning skills provided by the theoretical study of the teaching material, the course,
in particular the exercises, will stimulate the student to deepen his knowledge of some topics, to team working,
and to the concrete application of the concepts and techniques learned in the course.

Educational objectives

The course is intended to develop the capacity of formulating the optimization
problems underlying several design activities; to give the information
able to select suitable algorithms for the problem solution; to develop
new algorithms better performing than the available ones.

Educational objectives

The purpose of this course is to study mathematical models useful in taking decisions when more than a decision maker is present.
In particular variational inequalities and game theoretic models will be analyzed in detail. Emphasis will be given to computational aspects.
At the end of the course the student will be able to cast equilibrium problems as variational inequalities or by using game models and to develop suitable solution algorithms. He/She will also learn how to use game theoretic models and how to find algorithmically their equilibria and in realistic cases.

Knowledge and understanding
The aim of the course is to give students a basic knowledge of analytic methods for multi-agent environments analysis.
Students will be able to model mathematically complex interactions among (economic) agents and to analyze the consequences of different choices.

Applying knowledge and understanding
By the end of the course students will be able apply analytical tools to the analysis of economic and industrial settings.

Making judgements
Lectures and practical exercises will provide students with the ability toassess the main strengths and weaknesses of the learned theoretical models.
Students will also be able to gauge consequences of different decisionswhen using multi agent models.

Communication
By the end of the course, students will be able to discuss relevant information, ideas, problems and solutions both with a specialized and a non-specialized audience.

Lifelong learning skills
Students are expected to develop those learning skills necessary to undertake additional studieson the relevant topics with a high degree of autonomy. During the course, students are encouraged to investigate further topics of major interest by consulting supplementary academic publications, specialized books, and internet sites.

Educational objectives

Knowledge and understanding
The aim of the course is to introduce students to the application of optimization techniques to training problems arising in machine learning. Students are expected to gain insight into standard models in Machine Learning (Deep Networks and Support Vector Machines) and into more recent optimization algorithms for determining the parameters (training) of such models that best fit to the available data.

Applying knowledge and understanding
By the end of the course, students should be able to select the correct model for the problem at hand and either to use standard software specialized to the application and/or to develop their own optimization algorithm.
modelli di apprendimento automatico applicati ai casi studio in apprendimento automatico.

Making judgements
Lectures, practical exercises and project sessions will provide students with the ability to assess the main strengths and weaknesses of the different machine learning models applied to case studies in machine learning.

Communication
By the end of the course, students are able to point out the main features of a machine learning problem and explain techniques for its solution both with a specialized and a non-specialized audience. These abilities are tested and evaluated in the projects developed in small groups thus encouraging team building and a proactive learning process coupling with collaborative learning. These abilities can also be checked in the final oral exam.

Lifelong learning skills
Students are expected to develop those learning skills necessary to undertake additional studies on the relevant topics with a high degree of autonomy. During the classes, students are encouraged to work on projects into small groups thus stimulating student activity and engagement. They are pushed to consult supplementary research publications and internet sites to exploit tricks and detailed choices needed to accomplish the tasks effectively. These capabilities are tested and evaluated in the development of the final reports of the projects where students have to discuss the main issues of the addressed problems and their choices to overcome the difficulties, based on the topics and material covered in class.

Educational objectives

To provide some fundamental concepts of probability and statistics and to introduce some stochastic models for finance. Trying to enable the students to refine their critical aptitudes, to render them able to face not only "routine" problems, but also any "new" matter or situation.

Educational objectives

GENERAL OBJECTIVES OF THE COURSE:
The course, with an interdisciplinary approach, combines theoretical lectures on the economics of production, with lectures on the main econometric approaches proposed in the literature, including recent developments, and practical sessions to introduce to the main open source software available to carry out productivity and efficiency analysis.
The main objectives of the course are:
- Present a general overview on the economic theory of productivity and efficiency;
- Propose a unified framework on the main methodologies available to estimate and compare productivity and efficiency of Decision Making Units (DMUs);
- Make an introduction to the main open source software available to estimate productivity and efficiency;
- Provide laboratory sessions to implement productivity and efficiency analyses in practice;
- Provide the basic concepts to analyse the specialised literature;
- Interact with students through assisted laboratory and the realization of a practical work on real data, seminars and oral presentations.

SPECIFIC OBJECTIVES:
• knowledge and understanding: demonstrate the knowledge of the basic elements of productivity and efficiency analysis;
• ability to apply knowledge and understanding: to be able to apply efficiency analysis techniques learned during the course in its own engineering area of specialization;
• judgment autonomy: to be able to perform an efficiency analysis with critical spirit, choosing the appropriate method and correctly implementing it.
• communication skills: being able to communicate the results of the analysis and its information to different types of interlocutors;
• learning skills: to develop the necessary skills to apply and develop autonomously the methods learned during the course.

Educational objectives

The course will provide the foundations of theoretical, technical and practical aspects in the design and
implementation of machine learning systems, in particular neural networks based on the use of innovative
technologies such as Quantum Computing and Hyperdimensional Computing/Vector Symbolic Architectures, in
connection with the most advanced Deep Learning methodologies. The focus will be on the study of such
computational approaches for applications in the field of industrial and information engineering for the solution of
supervised and unsupervised problems in particular concerning optimization, approximation, regression,
interpolation, prediction, filtering, pattern recognition and classification. The main goal is to provide the student with
the ability to understand how to obtain advantages both from the quantum point of view (quantum advantage) and
from the use of distributed representations. The systems thus developed can then be used in applications related to
data-driven learning problems, i.e. in time series analysis, hyperdimensional computing and eXplainable AI,
considering the different real-world domains related to energy, aerospace, Earth observation, behavioral analysis,
bioengineering, finance, security, fraud detection and so on.

The main objective of the course is to enable students to develop hybrid and innovative systems mainly based on
Quantum Deep Neural Networks, distributed representations and hyperdimensional computing through an adequate
formulation of the problem, an appropriate choice of algorithms suitable for solving the problem itself and the
execution of experiments in laboratory activities so as to evaluate the effectiveness of the adopted techniques. The
applications of such systems will therefore be explored in vertical case studies such as, but not limited to, the
management of complex networks (smart grids, energy and goods distribution, biological and sociological networks,

etc.), the analysis of materials, the design of devices and circuits, automation and control systems, the inversion of
physical models and abstract organizational and decision-making models, telemedicine and so on.

Through a systematic laboratory activity, during which the methodologies related to the design and implementation of
quantum and hyperdimensional computing architectures will be taken into consideration, the student will integrate the
acquired knowledge to manage the complexity of inductive learning mechanisms and the real limits imposed by the
Noisy Intermediate-Scale Quantum (NISQ) quantum devices currently adopted, also and above all in function of the
application domains in the field of Industrial and Information Engineering that will be considered in the course. Particular
emphasis will be given to the understanding of the use of symbolic and hyperdimensional computing (HDC/VSA) for
efficient data processing, highlighting how this approach can offer significant computational and interpretative advantages compared to traditional methods.

Quantum technologies and quantum algorithms for information processing are rapidly evolving, considering the current
scenario based on short-term devices and hybrid quantum-classical approaches. Similarly, hyperdimensional and
(neuro)symbolic computing is establishing itself as a relevant paradigm for numerous applications with a high innovative
and technological coefficient. At the end of the course, the student will be able to communicate the knowledge acquired
to specialist and non-specialist interlocutors in the research and work fields in which he/she will carry out the subsequent
scientific and/or professional activity, also taking into account technological and sustainable development issues.

The adopted teaching methodology includes an autonomous and self-managed study activity during the development
of single-subject tasks, in a vertical manner on some specific theoretical and applicative topics using, for instance,
quantum resources available in the cloud such as IBM's Quantum Experience Platform, as well as quantum simulators
such as Qiskit, Pennylane and Flax in a Python environment, in addition to specific development frameworks for HDC
(TorchHD), all for the creation of machine learning systems applied to Industrial and Information Engineering
problems in the management, electrical, mechanical, logistics, biomedical fields and for the training of professional
and business skills capable of relating in the technical-scientific context of data analytics and business intelligence.

Educational objectives

GENERAL OBJECTIVES
The course aims to provide students with an in-depth understanding of the strategic and operational aspects of corporate purchasing management.
Through the analysis of purchasing strategies, spending analysis techniques, and purchasing portfolio matrices, the course seeks to develop the ability to effectively plan and manage the procurement process.
Particular attention will be devoted to the study of operational activities related to procurement, such as supplier qualification, the design of competitive tender procedures, quantitative analyses to support negotiations, the development of cost and remuneration models, and vendor rating systems.
At the end of the course, students will be able to apply tools and methodologies to develop purchasing strategies, select reliable and efficient suppliers, and define contractual agreements consistent with corporate needs.

SPECIFIC OBJECTIVES
Knowledge and understanding
Students will acquire:
-knowledge of the main sourcing strategies and purchasing portfolio matrices;
-understanding of procurement processes and operational phases, including supplier qualification, tender management, cost model development, and vendor rating.

Applying knowledge and understanding
Students will be able to:
-analyze corporate spending and classify product groups;
-develop purchasing strategies tailored to specific supply segments;
-select suppliers using quantitative and qualitative tools;
-design competitive tender procedures;
-support the negotiation process and contract design through structured analyses.

Making judgements
The course will develop the students’ ability to:
-critically evaluate sourcing strategies based on market and organizational characteristics;
-independently select analytical methods and operational tools for procurement management.

Communication skills
At the end of the course, students will be able to:
-clearly and systematically present procurement strategies and decisions;
-draft technical documents and analysis reports using specialized terminology and internationally recognized models.

Learning skills
Students will develop autonomous learning abilities, enabling them to:
-deepen their knowledge of advanced purchasing management methodologies;
-stay updated on the evolution of procurement practices and tools in dynamic and complex environments.

Educational objectives

###################
General Objectives:
###################

Major advances in technology have resulted in the widespread implementation of information systems into businesses and organizations.

This course introduces languages, principles and methods of process modeling, analysis and innovation as critical factors to the
overall success of a business.

The course centers around the role of conceptual (sometimes referred as business) process modeling as a means to understand and capture the
workflows of interest in information systems of various kind. Students will learn the elements of process models and their precise meaning
using the Business Process Model and Notation (BPMN) international standard.

The course will cover processes within organizations (process orchestrations) and also interacting processes involving several organizations
(process choreographies), and will look at techniques to analyze and improve such processes from a formal perspective.

The course will also provide a basic knowledge and understanding of how to design, test and implement information systems for executable processes.

Finally, the course will present methods and tools to properly use process mining techniques, which enable to discover process models (whose structure
is unknown at the outset) starting from the logs recording the concrete events executed by the real workflows.

####################
Specific Objectives:
####################

----------------------------
Knowledge and understanding:
----------------------------
At the end of the course, the students:
- learn the main methods to carry out a BPM (Business Process Management) project;
- are able to model a process with the BPMN standard;
- are able to implement and execute a process through a real information system;
- understand process mining algorithms and techniques.

-------------------------------------
Applying knowledge and understanding:
-------------------------------------
The students will be able to use suitable methodological and technological solutions for
(i) modeling a process in BPMN;
(ii) analysing it with quantitative techniques;
(iii) executing and monitoring it with an information system.

------------------
Making judgements:
------------------
The student acquires autonomy of judgment in proposing the most suitable approach to carry out a BPM project.

--------------
Communication:
--------------
The project activities and the lectures of the course allow the students to develop the proper abilities to communicate/share the design choices and
development methods for realizing any step of the business process life-cycle.

-------------------------
Lifelong learning skills:
-------------------------
In addition to the traditional learning skills provided by studying the teaching material, the project activities stimulate the student to
deepen her knowledge of the BPM topic, to improve the teamwork, and to the concrete application of the concepts and techniques investigated
during the course.

Educational objectives

Knowledge and understanding
The course introduces students to the economics and management of networks. On the one hand, the course illustrates the main features of the new information economy, and discusses the prevailing and emerging business models. On the other hand, it explores competition and regulation issues in liberalized network industries, such as telecommunications, energy, and transportation.

Applying knowledge and understanding
Students are expected to be able to use methods and models of microeconomics and industrial organization to understand and analyze the impact of technology and demand on market structure, firms’ strategies and business models in the new information economy. They will also gain insight on the rationale and the scope for public policy in network industries.

Making judgements
Lectures, practical exercises and problem-solving sessions will provide students with the ability to assess the main strengths and weaknesses of theoretical models when used to explain empirical evidence and case studies in the new information economy and in network industries.

Communication
By the end of the course, students are able to point out the main features of the new information economy and network industries, and to discuss relevant information, ideas, problems and solutions both with a specialized and a non-specialized audience. These capabilities are tested and evaluated in the final written exam and possibly in the oral exam as well as in the project work.

Lifelong learning skills
Students are expected to develop those learning skills necessary to undertake additional studies on relevant topics in the field of the new information economy and network industries with a high degree of autonomy. During the course, students are encouraged to investigate further any topics of major interest, by consulting supplementary academic publications, specialized books, and internet sites. These capabilities are tested and evaluated in the final written exam and possibly in the oral exam as well as in the project work, where students may have to discuss and solve some new problems based on the topics and material covered in class.

Educational objectives

Knowledge and understanding
Students will acquire advanced knowledge of financial market microstructure, algorithmic trading concepts, and the statistical foundations of quantitative strategies. They will thoroughly understand how to translate theoretical trading ideas into computational logic using programming languages such as Python and EasyLanguage.

2) Applying knowledge and understanding
Students will be able to apply theoretical concepts to practically build, test, and optimize automated trading systems. They will use Python for advanced data analysis, statistical modeling, and machine learning integration, and will employ EasyLanguage for rapid strategy prototyping, platform integration, and market execution.

3) Making judgements
Students are encouraged to critically discuss quantitative trading strategies, market anomalies, and algorithmic models in the classroom. The course includes practical sessions in which students apply the acquired knowledge to design, test, and evaluate empirical trading models. Using historical and live market data, students will critically assess the performance and robustness of their strategies implemented through EasyLanguage and Python, learning to distinguish between genuine predictive power and data overfitting.

4) Communication skills
At the end of the course, students will be able to illustrate and explain algorithmic logic, risk metrics (e.g., Sharpe ratio, maximum drawdown), and the strengths and weaknesses of various trading methodologies to a variety of heterogeneous interlocutors (such as investors, risk managers, or software developers). The acquisition of these skills is verified and evaluated not only during the final exam, but also during project presentations and assessments in which, individually or in groups, students are called to present their own trading algorithms and showcase comprehensive backtesting reports.

5) Learning skills
Students acquire the ability to independently research, design, and code quantitative trading systems using Python and EasyLanguage. The tools provided by the course allow for the analysis of complex financial markets, enabling students to build models that simultaneously account for multiple market factors, execution costs, and the strict risk management inherent in algorithmic trading.

Educational objectives

The course aims to describe the founding principles, the scope and the fundamental toolsand
methodologies of project management and of organizational processes.
Starting from the concept of integrated management of projects, all the
main methods for managing the performance variables of quality, time and
cost will be proposed. In line with the main standard processes of
project management the internationally standardized project management
terminology will be used. The course also intends to present the
principles and techniques of business process re-engineering and
management.Risultati di apprendimento attesi (Inglese):At the
end of the course the student will be able to plan a project starting
from the objectives of quality, time and cost defined by internal or
external customers of a company. She/he will also be able to critically
analyze an ongoing project proposing both organizational and managerial
improvements and both the use of correct project management
methodologies.

Educational objectives

The course gives on-field experience of Operations Management, from an operational and technical point of view. The course develops one of the main topics of Operations Management, e.g. company-market relations, supply chain management, production and inventory management.

EXPECTED LEARNING OUTCOMES. Knowledge and understanding: Deepening and consolidation of the knowledge provided in the Operations Management course. Capability: Expected learning outcomes include the ability to develop analyses, model problems, and identify the best techniques for solving major characteristic problems in production management, materials management, logistics management, and supply chain management through project work and on-field experience.

Educational objectives

The objective is to provide the management engineer with the basis for understanding the theory and principles of innovative optical, photothermal, photoacoustic, radiometric and thermographic techniques and instruments, and to understand the main applications together with their industrial relapses and with a deep analysis of the economical impacts and parameters.

The topics are organized to provide scientific technical expertise in order to work in the field of applied research, making easier the employment of the new engineer in industry.

Contents of the proposed course:

The course will provide the theoretical bases of the optical systems, the laser with its applications. Photothermal, photoacoustic, radiometric, and infrared techniques will be introduced for nonsdestructive evaluation and testing of materials, together with the many applications in different fields: industry, environment, energy, but also biology, medicine, and in the emerging sector of agri-food. Final comparisons will be introduced among the diagnostic techniques by using parameters of the management engeneering. The course contains also some experimental activities in laboratory and the data analysis and processing.

Educational objectives

The course aims at deepening the design, the implementation, the execution and the analysis of the results of
discrete event simulation models. Particular focus will be on the use of specialized software packages and languages
for discrete event simulation and their possible use in connection with optimization software. Students will be
directly involved in laboratory activities and in particular in the implementation of real case studies.

Knowledge and understanding:
Techniques for implementing a discrete event simulation model. Use of software packages dedicated to
discrete event simulation and their possible integration with optimization software.

Applying knowledge and understanding:
To be able to carry out a complete study of a real problem through discrete event simulation, from the preliminary analysis
of the processes, the implementation, up to the interpretation of the results.

Making judgements:
Knowing how to identify the fundamental characteristics of a real process for the construction of a model of
discrete event simulation. Knowing how to evaluate the impact of the output of a simulation on the real case in analysis.

Communication skills:
The laboratory activities will allow the student to learn communicating and sharing simulation models and their outputs.

Learning skills:
The laboratory activities will stimulate the students to the autonomous realization of simulation models, to teamwork,
and to the concrete application of the techniques learned in the course.

Educational objectives

Main course’s objective is
developing digital competences to make citizens able to use Information
and Communication Technologies (ICT) at work, in day-to-day relationships, in
dealing with public services as well as in culture, entertainment, leisure and
when participating in community and political dialogues. This course contributes
to actualize the EU i2010 program “increasing social
inclusion through reducing digital divide to build a society for
all”. The course promotes ICT use also to put in action the Italian Digital
Administration Low as well as the ICT right use (art.3) and the Public
Administration Data Availability (art 50 capo V).

Educational objectives

The specific goal of these activities is to enable the students to merge their academic knowledge with professional skills.

Educational objectives

The specific aim is to allow the student to use and expand the bulk of knowledge acquired during the course of study performing some activities in an industrial setting, a company, or a research laboratory.

Educational objectives

GENERAL OBJECTIVES

The course aims to provide students with an integrated understanding of the fundamental tools for
monitoring, improving, and innovating production processes. Specifically, the objective is to
understand the use of Statistical Process Control (SPC), Design of Experiments (DOE), and Life
Cycle Assessment (LCA) as strategic levers for optimizing quality, efficiency, and sustainability.
Through a theoretical-practical approach and the analysis of case studies, the course aims to
develop critical thinking in the combined application of these tools, promoting a systemic and
conscious vision of innovation in manufacturing contexts.

SPECIFIC OBJECTIVES – KNOWLEDGE AND UNDERSTANDING
The course will enable students to acquire the logic and principles governing quality monitoring in
production processes, efficient experiment design for performance optimization, and the systemic
approach to sustainability. Particular attention will be given to understanding technical standards,
analytical methods (e.g., control charts, factorial designs, environmental impact assessments), and
the integration of statistical, experimental, and environmental tools to support informed industrial
decision-making.
APPLICATIVE SKILLS
The theoretical knowledge acquired during the course, applied to real case studies through hands-
on exercises using specialized software (Minitab, openLCA, GaBi), will enable students to design
control charts, conduct DOE analyses, build models to assess environmental impact, and propose
innovative solutions for process optimization.

JUDGMENT AUTONOMY
The course fosters the development of critical thinking through the comparative analysis of
different methodologies, the assessment of their applications, and the discussion of real production
scenarios. Students learn to evaluate data reliability, model robustness, and the appropriateness of
techniques in relation to specific production contexts.

COMMUNICATION SKILLS
At the end of the course, students will be able to effectively communicate the results of statistical,
experimental, and environmental analyses to both technical and non-technical audiences, clearly
explaining their strengths and weaknesses. Communication skills will be exercised during group
discussions and in the technical documentation related to case studies.

LEARNING ABILITY
The course will enhance the ability of students to independently learn new methodologies and tools
in the field of process control and innovation, even in multidisciplinary contexts.

Educational objectives

This course aims providing knowledges and abilities in the field of Additive Manufacturing with regards to:

- Knowledge about several technologies by means of physical and chemical processes;

- Knowledge about design methods applied to the virtual model and its reparing;

- Ability to manage different processes choosing suitable parameters and fabrication strategies;

- Ability to plan, before the AM fabrication, post processing operation in order to comply with the design requirements;

- Ability to compare additive and traditional processes in order to choose the better one for a specific case;

- Ability to apply the acquired knowledges to case studies;

- Ability to provide a technological transfer.

Educational objectives

ENG
GENERAL OBJECTIVES
The course aims to provide students with a fundamental understanding of concepts and tools relevant to marketing. Specifically, the course aims to help students understand: the main forces of the marketing environment; the marketing information system; consumer and business buying behaviors; the stages and tools for formulating and implementing a marketing strategy. Furthermore, through the analysis of a series of case studies, the course aims to develop students' critical analysis skills, enabling them to interpret and explain business behavior and outcomes within marketing strategies in light of the concepts learned during the course.

SPECIFIC OBJECTIVES
KNOWLEDGE AND UNDERSTANDING. The course will enable students to acquire knowledge and understanding of the main concepts and fundamental tools of Marketing. Students will learn to recognize and master best practices and success factors of Marketing and apply them in real-world contexts.

APPLICATIVE SKILLS. Thanks to the course, students will be able to formulate a marketing plan, as well as critically evaluate marketing strategies.

JUDGMENT AUTONOMY. The course will empower students to choose, given the main environmental forces, the characteristics of the enterprise and innovation, the best marketing strategies. Additionally, students will develop the ability to critically analyze marketing.

COMMUNICATION SKILLS. By the end of the course, students will be able to explain marketing concepts using internationally established terminology and models, organize information and data in a format and reporting process understandable to professionals.

LEARNING ABILITY. Students will develop independent study skills and critical understanding and evaluation of marketing strategies and related tools.

Educational objectives

1) Knowledge and understanding
During the lessons of Data-driven Economics, students acquire the basic theoretical elements of econometric analysis. Theoretical lessons are aimed at guiding students in the acquisition of the basics of simple and multiple regression models, starting from the relative assumptions, and then proceeding with the estimation and inference procedures. The course contents cover both the estimation of linear and non-linear models and the analysis of both cross-sectional and longitudinal data.
2) Applicate applying knowledge and understanding
The students of the Data-driven Economics course are able to apply the notions acquired during the theoretical lessons to a wide range of problems of an empirical nature. They acquire the ability to build econometric models aimed at giving empirical content to economic relations and are also able to establish a causal link between two or more variables in the economic field.
3) Making judgements
Students are encouraged to critically discuss empirical studies published in the economic/managerial field in the classroom. The Data-driven Economics course also includes a laboratory in which students apply the acquired knowledge of econometrics to the estimation of empirical models carried out using data made available by the teacher.
4) Communication skills
At the end of the course, students are able to illustrate and explain the strengths and weaknesses of a wide range of empirical methodologies to a variety of heterogeneous interlocutors in terms of training and professional role. The acquisition of these skills is verified and evaluated not only during the final exam, by means of a written test and a possible oral test, but also during flipped class sessions in which, individually or in groups, students are called to present empirical studies published in the economic/managerial field.
5) Learning skills
Students acquire the ability to independently conduct empirical analyses by building econometric models to be estimated using data with diversified structures. The tools provided by the course allow for the analysis of systems in which a large number of factors simultaneously contribute to explaining their states and impact assessments that take into account the uncertainty and risk inherent in the application of policies. The acquisition of these skills is verified and evaluated on during the final exam, by means a written test and a possible oral test, in which the student can be called to discuss empirical problems on the basis of the topics covered and the reference material distributed during the course.

Educational objectives

GENERAL OBJECTIVES
The course aims to provide students with a basic understanding of concepts and tools relevant to Innovation Management. Specifically, the course aims to help students understand: the forms, models, and sources of innovation; standard conflicts and the definition of dominant design; market entry timing choices; innovation protection mechanisms; the process of developing a new product; the integration of environmental sustainability into marketing strategy and new product development. Furthermore, through the analysis of a series of case studies, the course aims to develop students' critical analysis skills, enabling them to interpret and explain business behavior and outcomes within the context of technological innovation strategies in light of the concepts learned during the course.

SPECIFIC OBJECTIVES
KNOWLEDGE AND UNDERSTANDING. The course will enable students to acquire knowledge and understanding of the main concepts and fundamental tools of Innovation Management. Students will learn to recognize and master best practices and success factors of Innovation Management and apply them in real-world contexts.

APPLICATIVE SKILLS. Thanks to the course, students will be able to critically evaluate an enterprise's technological innovation strategies, as well as classify products based on their environmental impact.

JUDGMENT AUTONOMY. The course will empower students to choose, given the main environmental forces, the characteristics of the enterprise and innovation, the best technological innovation strategies. Additionally, students will develop the ability to critically analyze innovation management.

COMMUNICATION SKILLS. By the end of the course, students will be able to illustrate concepts of innovation management using internationally established terminology and models, organize information and data in a format and reporting process understandable to professionals.

LEARNING ABILITY. Students will develop independent study skills and critical understanding and evaluation of marketing and technological innovation strategies and related tools

Educational objectives

GENERAL OBJECTIVES
The Financial Markets course aims to provide students with a solid understanding of the modern
financial markets, the main characteristics of different securities, their valuation and risk
management. The course covers fundamental financial concepts such as the time value of money,
risk – return relationship, the term structure of interest rates, and pricing models for bonds, stocks
and derivatives. The course also provides some basic hedging strategies involving financial
derivatives.
�� SPECIFIC OBJECTIVES
 Knowledge and understanding
Students will gain a comprehensive understanding of the core principles of finance,

financial contracts. They will become familiar with the theoretical models used to value
financial instruments and assess the associated risks.
 Applying knowledge and understanding
Students will be able to apply the acquired concepts to evaluate financial assets, analyze
the interest rate term structures, interpret the price movements of stocks and bonds, use
models such as CAPM and APT in portfolio management. They will also be able to use
basic derivative strategies for hedging purposes.
 Making judgements
The course will help students develop critical thinking in evaluating financial decisions and
risk management strategies. They will learn to assess the consistency and appropriateness
of financial tools and strategies in different market conditions.
 Communication skills
Students will be able to clearly communicate technical financial concepts, both in written
and oral form, using appropriate terminology and supporting their arguments with
quantitative data and analytical models.
 Learning skills
The course will foster students’ ability to independently study more advanced financial
topics. They will acquire the foundational knowledge required for further specialization and
continuous learning in the field of finance.

Educational objectives

ENG
GENERAL OBJECTIVES
The course aims to provide students with a fundamental understanding of concepts and tools relevant to marketing. Specifically, the course aims to help students understand: the main forces of the marketing environment; the marketing information system; consumer and business buying behaviors; the stages and tools for formulating and implementing a marketing strategy. Furthermore, through the analysis of a series of case studies, the course aims to develop students' critical analysis skills, enabling them to interpret and explain business behavior and outcomes within marketing strategies in light of the concepts learned during the course.

SPECIFIC OBJECTIVES
KNOWLEDGE AND UNDERSTANDING. The course will enable students to acquire knowledge and understanding of the main concepts and fundamental tools of Marketing. Students will learn to recognize and master best practices and success factors of Marketing and apply them in real-world contexts.

APPLICATIVE SKILLS. Thanks to the course, students will be able to formulate a marketing plan, as well as critically evaluate marketing strategies.

JUDGMENT AUTONOMY. The course will empower students to choose, given the main environmental forces, the characteristics of the enterprise and innovation, the best marketing strategies. Additionally, students will develop the ability to critically analyze marketing.

COMMUNICATION SKILLS. By the end of the course, students will be able to explain marketing concepts using internationally established terminology and models, organize information and data in a format and reporting process understandable to professionals.

LEARNING ABILITY. Students will develop independent study skills and critical understanding and evaluation of marketing strategies and related tools.

Educational objectives

To provide some fundamental concepts of probability and statistics and to introduce some stochastic models for finance. Trying to enable the students to refine their critical aptitudes, to render them able to face not only "routine" problems, but also any "new" matter or situation.

Educational objectives

GENERAL OBJECTIVES

The aim of the course is to introduce Project Finance as a tool for the development, financing and construction of infrastructures, both in public and private sectors. In particular, the course, through the analysis of the different players involved and their respective roles, the description of the main risks in the various phases of a project and the possible mitigations, as well as the several financial sources that can be accessed to support an initiative (equity, financial debt, public resources) will provide students with the tools necessary to understand the main drivers underlying the structuring of a Project Finance transaction. Finally, all the several aspects of a Project Finance transaction (technical, economic, financial, legal and organizational) will be assessed, focusing on how they should be properly weighted and addressed to identify the most suitable financial structure to support a Project Finance initiative, with the aim of ensuring its sustainability in compliance with the "bankability" criteria. During the course, business cases will be discussed and exercises will be carried out in a Windows Excel environment, aimed at developing a Project Finance financial model with which to evaluate the economic/financial sustainability and the bankability of an investment. The course may be integrated with testimonials provided by leading national and international professionals and operators in the sector.

SPECIFIC OBJECTIVES
KNOWLEDGE AND UNDERSTANDING. The course aims to provide students with a general overview of Project Finance, in order to understand the main actors involved and the relevant roles played, the sectors where Project finance can be used and the characteristics/requirements that a project must possess in order to be classified as a Project Finance initiative. The prerogative of the course is also to provide students with the quantitative tools necessary to evaluate the economic/financial sustainability of an initiative, with particular reference to economic/financial and bankability indicators.

APPLICATIVE SKILLS. At the end of the course the student will be able to evaluate whether a project has the features to be structured using the Project Finance technique; the student will also be able to set up business plans in a Windows Excel environment with which to evaluate an investment and define the most suitable financial structure to support it, in compliance with the profitability/bankability criteria.

JUDGMENT AUTONOMY. The course will enable students to judge the feasibility and financial sustainability of a Project Finance transaction, through the identification of the right mix of financial sources (equity/debt), with which to satisfy the bankability criteria underlying a Project Finance deal.

COMMUNICATION SKILLS. At the end of the course the student will be able to understand and use the main technical and financial terms used when structuring a Project Finance deal and expose the key features and requirements in using this financial technique. He will also be able to summarize the main KPIs of a transaction to demonstrate its sustainability and bankability.

LEARNING ABILITY. The student will develop independent study skills and understanding and critical evaluation of the variables underlying the structuring of a deal in Project Finance.

Educational objectives

The purpose of this course is to study mathematical models useful in taking decisions when more than a decision maker is present.
In particular variational inequalities and game theoretic models will be analyzed in detail. Emphasis will be given to computational aspects.
At the end of the course the student will be able to cast equilibrium problems as variational inequalities or by using game models and to develop suitable solution algorithms. He/She will also learn how to use game theoretic models and how to find algorithmically their equilibria and in realistic cases.

Knowledge and understanding
The aim of the course is to give students a basic knowledge of analytic methods for multi-agent environments analysis.
Students will be able to model mathematically complex interactions among (economic) agents and to analyze the consequences of different choices.

Applying knowledge and understanding
By the end of the course students will be able apply analytical tools to the analysis of economic and industrial settings.

Making judgements
Lectures and practical exercises will provide students with the ability toassess the main strengths and weaknesses of the learned theoretical models.
Students will also be able to gauge consequences of different decisionswhen using multi agent models.

Communication
By the end of the course, students will be able to discuss relevant information, ideas, problems and solutions both with a specialized and a non-specialized audience.

Lifelong learning skills
Students are expected to develop those learning skills necessary to undertake additional studieson the relevant topics with a high degree of autonomy. During the course, students are encouraged to investigate further topics of major interest by consulting supplementary academic publications, specialized books, and internet sites.

Educational objectives

The course will provide the foundations of theoretical, technical and practical aspects in the design and
implementation of machine learning systems, in particular neural networks based on the use of innovative
technologies such as Quantum Computing and Hyperdimensional Computing/Vector Symbolic Architectures, in
connection with the most advanced Deep Learning methodologies. The focus will be on the study of such
computational approaches for applications in the field of industrial and information engineering for the solution of
supervised and unsupervised problems in particular concerning optimization, approximation, regression,
interpolation, prediction, filtering, pattern recognition and classification. The main goal is to provide the student with
the ability to understand how to obtain advantages both from the quantum point of view (quantum advantage) and
from the use of distributed representations. The systems thus developed can then be used in applications related to
data-driven learning problems, i.e. in time series analysis, hyperdimensional computing and eXplainable AI,
considering the different real-world domains related to energy, aerospace, Earth observation, behavioral analysis,
bioengineering, finance, security, fraud detection and so on.

The main objective of the course is to enable students to develop hybrid and innovative systems mainly based on
Quantum Deep Neural Networks, distributed representations and hyperdimensional computing through an adequate
formulation of the problem, an appropriate choice of algorithms suitable for solving the problem itself and the
execution of experiments in laboratory activities so as to evaluate the effectiveness of the adopted techniques. The
applications of such systems will therefore be explored in vertical case studies such as, but not limited to, the
management of complex networks (smart grids, energy and goods distribution, biological and sociological networks,

etc.), the analysis of materials, the design of devices and circuits, automation and control systems, the inversion of
physical models and abstract organizational and decision-making models, telemedicine and so on.

Through a systematic laboratory activity, during which the methodologies related to the design and implementation of
quantum and hyperdimensional computing architectures will be taken into consideration, the student will integrate the
acquired knowledge to manage the complexity of inductive learning mechanisms and the real limits imposed by the
Noisy Intermediate-Scale Quantum (NISQ) quantum devices currently adopted, also and above all in function of the
application domains in the field of Industrial and Information Engineering that will be considered in the course. Particular
emphasis will be given to the understanding of the use of symbolic and hyperdimensional computing (HDC/VSA) for
efficient data processing, highlighting how this approach can offer significant computational and interpretative advantages compared to traditional methods.

Quantum technologies and quantum algorithms for information processing are rapidly evolving, considering the current
scenario based on short-term devices and hybrid quantum-classical approaches. Similarly, hyperdimensional and
(neuro)symbolic computing is establishing itself as a relevant paradigm for numerous applications with a high innovative
and technological coefficient. At the end of the course, the student will be able to communicate the knowledge acquired
to specialist and non-specialist interlocutors in the research and work fields in which he/she will carry out the subsequent
scientific and/or professional activity, also taking into account technological and sustainable development issues.

The adopted teaching methodology includes an autonomous and self-managed study activity during the development
of single-subject tasks, in a vertical manner on some specific theoretical and applicative topics using, for instance,
quantum resources available in the cloud such as IBM's Quantum Experience Platform, as well as quantum simulators
such as Qiskit, Pennylane and Flax in a Python environment, in addition to specific development frameworks for HDC
(TorchHD), all for the creation of machine learning systems applied to Industrial and Information Engineering
problems in the management, electrical, mechanical, logistics, biomedical fields and for the training of professional
and business skills capable of relating in the technical-scientific context of data analytics and business intelligence.

Educational objectives

GENERAL OBJECTIVES
The course aims to provide students with a basic understanding of concepts and tools relevant to Innovation Management. Specifically, the course aims to help students understand: the forms, models, and sources of innovation; standard conflicts and the definition of dominant design; market entry timing choices; innovation protection mechanisms; the process of developing a new product; the integration of environmental sustainability into marketing strategy and new product development. Furthermore, through the analysis of a series of case studies, the course aims to develop students' critical analysis skills, enabling them to interpret and explain business behavior and outcomes within the context of technological innovation strategies in light of the concepts learned during the course.

SPECIFIC OBJECTIVES
KNOWLEDGE AND UNDERSTANDING. The course will enable students to acquire knowledge and understanding of the main concepts and fundamental tools of Innovation Management. Students will learn to recognize and master best practices and success factors of Innovation Management and apply them in real-world contexts.

APPLICATIVE SKILLS. Thanks to the course, students will be able to critically evaluate an enterprise's technological innovation strategies, as well as classify products based on their environmental impact.

JUDGMENT AUTONOMY. The course will empower students to choose, given the main environmental forces, the characteristics of the enterprise and innovation, the best technological innovation strategies. Additionally, students will develop the ability to critically analyze innovation management.

COMMUNICATION SKILLS. By the end of the course, students will be able to illustrate concepts of innovation management using internationally established terminology and models, organize information and data in a format and reporting process understandable to professionals.

LEARNING ABILITY. Students will develop independent study skills and critical understanding and evaluation of marketing and technological innovation strategies and related tools

Educational objectives

General outcomes:

The course will present a broad survey of topics at the interface of computer
science, data science, and economics, emphasizing efficiency, robustness, and application to emerging online markets. It will introduce the principles of algorithmic game theory and mechanism design, algorithmic market design, as well as machine learning in games and markets. It will demonstrate applications to case studies in Web search and advertising, network economics, Data, cryptocurrency, and AI markets.

Specific outcomes:
Knowledge and understanding:
The algorithmic and mathematical economics principles underlying the design and the operation of efficient and robust online markets. The application of these principles in concrete examples of online markets.

Applying knowledge and understanding:
Being able to design and analyze algorithms for concrete online market applications with respect to the requirements of efficiency and robustness.

Making judgements:
Being able to evaluate the quality of an algorithm for online market applications, discriminating the modeling aspects from those related to algorithmic and system implementation.

Communication skills:
Ability to communicate and share the modeling choices and system requirements, as well as the results of the analysis of the efficiency of online market algorithms.

Learning skills:
The course stimulates the students to acquire learning skills at the crossroads of computer science, economics, and digital market applications, including the different languages used in these fields.

Educational objectives

Devising new machine learning methods and statistical models is a fun and extremely fruitful “art”. But these powerful tools are not useful unless we understand when they work, and when they fail. The main goal of statistical learning theory is thus to study, in a
statistical framework, the properties of learning algorithms mainly in the form of so-called error bounds.
This course introduces the techniques that are used to obtain such results, combining methodology with theoretical foundations and computational aspects. It treats both the basic principles to design successful learning algorithms and the “science” of analyzing an algorithm’s statistical properties and performance guarantees.
Theorems are presented together with practical aspects of methodology and intuition to help students develop tools for selecting appropriate methods and approaches to problems in their own data analyses.
Methods for a wide variety of applied problems will be explored and implemented on open-source software like R (www.r-project.org), Keras (https://keras.io/) and TensorFlow (https://www.tensorflow.org/).
Knowledge and understanding

On successful completion of this course, students will:
know the main learning methodologies and paradigms with their strengths and weakness;
be able to identify a proper learning model for a given problem;
assess the empirical and theoretical performance of different learning models;
know the main platforms, programming languages and solutions to develop effective implementations.

Applying knowledge and understanding

Besides the understanding of theoretical aspects, thanks to applied homeworks and a final project possibly linked to hackathons or other data analysis competitions, the students will constantly be challenged to use and evaluate modern learning techniques and algorithms.

Making judgements

On successful completion of this course, students will develop a positive critical attitude towards the empirical and theoretical evaluation of statistical learning paradigms and techniques.

Communication skills

In preparing the report and oral presentation for the final project, students will learn how to effectively communicate original ideas, experimental results and the principles behind advanced data analytic techniques in written and oral form. They will also understand how to offer constructive critiques on the presentations of their peers.

Learning skills

In this course the students will develop the skills necessary for a successful understanding as well as development of new learning methodologies together with their effective implementation. The goal is of course to grow a active attitude towards continued learning throughout a professional career.

Educational objectives

Knowledge and understanding
The course introduces students to the economics and management of networks. On the one hand, the course illustrates the main features of the new information economy, and discusses the prevailing and emerging business models. On the other hand, it explores competition and regulation issues in liberalized network industries, such as telecommunications, energy, and transportation.

Applying knowledge and understanding
Students are expected to be able to use methods and models of microeconomics and industrial organization to understand and analyze the impact of technology and demand on market structure, firms’ strategies and business models in the new information economy. They will also gain insight on the rationale and the scope for public policy in network industries.

Making judgements
Lectures, practical exercises and problem-solving sessions will provide students with the ability to assess the main strengths and weaknesses of theoretical models when used to explain empirical evidence and case studies in the new information economy and in network industries.

Communication
By the end of the course, students are able to point out the main features of the new information economy and network industries, and to discuss relevant information, ideas, problems and solutions both with a specialized and a non-specialized audience. These capabilities are tested and evaluated in the final written exam and possibly in the oral exam as well as in the project work.

Lifelong learning skills
Students are expected to develop those learning skills necessary to undertake additional studies on relevant topics in the field of the new information economy and network industries with a high degree of autonomy. During the course, students are encouraged to investigate further any topics of major interest, by consulting supplementary academic publications, specialized books, and internet sites. These capabilities are tested and evaluated in the final written exam and possibly in the oral exam as well as in the project work, where students may have to discuss and solve some new problems based on the topics and material covered in class.

Educational objectives

1. Knowledge and Understanding

On successful completion of the course, students will have demonstrated knowledge and understanding of the foundational principles of sustainable development, circular economy, and Life Cycle Management, including their strategic and tactical implications for operations management and the green governance of the entire supply chain. They will have demonstrated knowledge and understanding of the principles and instruments of corporate environmental governance – including Environmental Management Systems (EMS), statutory reporting requirements, product environmental declarations, and sustainability certifications – as well as of the tools for servitization and digitalization required to guide the transition of traditional production systems towards resilient and eco-efficient product-service models.

2. Applying Knowledge and Understanding

Students will be able to apply their knowledge and understanding of circular economy and Life Cycle Management tools and best practices in real-world industrial contexts, supported by the analysis of case studies. They will be able to apply their understanding of servitization (Product-Service Systems) methods and digital tools to innovate business models and optimize industrial production systems.

3. Making Judgements

Students will have the ability to gather and interpret relevant data in order to critically assess the environmental, economic, and organisational implications of operational decisions along the supply chain, coherently integrating managerial-organisational and technical-operational perspectives. They will be able to form independent judgements on the adequacy of technological and managerial solutions with respect to an organisation’s ecological transition objectives, including reflection on relevant social and ethical dimensions.

4. Communication

Students will be able to communicate information, ideas, problems, and solutions related to industrial sustainability to both specialist and non-specialist audiences, using appropriate technical language. They will be able to draft reports in compliance with environmental reporting and certification standards, as well as to present complex case studies by effectively integrating engineering, managerial, and environmental perspectives.

5. Learning Skills

Students will have developed the learning skills necessary to undertake further study and to independently update their knowledge in response to regulatory, technological, and market developments in the field of industrial sustainability, through critical engagement with scientific literature and industry standards. They will have acquired a mindset of continuous improvement that enables them to transfer the methodological tools developed during the course to new contexts and to integrate emerging approaches with their established knowledge base.

Educational objectives

GENERAL OBJECTIVES

The aim of the course is to introduce Project Finance as a tool for the development, financing and construction of infrastructures, both in public and private sectors. In particular, the course, through the analysis of the different players involved and their respective roles, the description of the main risks in the various phases of a project and the possible mitigations, as well as the several financial sources that can be accessed to support an initiative (equity, financial debt, public resources) will provide students with the tools necessary to understand the main drivers underlying the structuring of a Project Finance transaction. Finally, all the several aspects of a Project Finance transaction (technical, economic, financial, legal and organizational) will be assessed, focusing on how they should be properly weighted and addressed to identify the most suitable financial structure to support a Project Finance initiative, with the aim of ensuring its sustainability in compliance with the "bankability" criteria. During the course, business cases will be discussed and exercises will be carried out in a Windows Excel environment, aimed at developing a Project Finance financial model with which to evaluate the economic/financial sustainability and the bankability of an investment. The course may be integrated with testimonials provided by leading national and international professionals and operators in the sector.

SPECIFIC OBJECTIVES
KNOWLEDGE AND UNDERSTANDING. The course aims to provide students with a general overview of Project Finance, in order to understand the main actors involved and the relevant roles played, the sectors where Project finance can be used and the characteristics/requirements that a project must possess in order to be classified as a Project Finance initiative. The prerogative of the course is also to provide students with the quantitative tools necessary to evaluate the economic/financial sustainability of an initiative, with particular reference to economic/financial and bankability indicators.

APPLICATIVE SKILLS. At the end of the course the student will be able to evaluate whether a project has the features to be structured using the Project Finance technique; the student will also be able to set up business plans in a Windows Excel environment with which to evaluate an investment and define the most suitable financial structure to support it, in compliance with the profitability/bankability criteria.

JUDGMENT AUTONOMY. The course will enable students to judge the feasibility and financial sustainability of a Project Finance transaction, through the identification of the right mix of financial sources (equity/debt), with which to satisfy the bankability criteria underlying a Project Finance deal.

COMMUNICATION SKILLS. At the end of the course the student will be able to understand and use the main technical and financial terms used when structuring a Project Finance deal and expose the key features and requirements in using this financial technique. He will also be able to summarize the main KPIs of a transaction to demonstrate its sustainability and bankability.

LEARNING ABILITY. The student will develop independent study skills and understanding and critical evaluation of the variables underlying the structuring of a deal in Project Finance.

Educational objectives

Student must will able to:
- choose an additive manufacturing technology in order to comply with product specifications
- apply the Design for Additive Manufacturing
- analyze a CNC code
- employ a Computer Aided Manufacturing
- plan a Flexible Manufacturing System and a Flexible Assembly System

Educational objectives

GENERAL OBJECTIVES OF THE COURSE:
The course, with an interdisciplinary approach, combines theoretical lectures on the economics of production, with lectures on the main econometric approaches proposed in the literature, including recent developments, and practical sessions to introduce to the main open source software available to carry out productivity and efficiency analysis.
The main objectives of the course are:
- Present a general overview on the economic theory of productivity and efficiency;
- Propose a unified framework on the main methodologies available to estimate and compare productivity and efficiency of Decision Making Units (DMUs);
- Make an introduction to the main open source software available to estimate productivity and efficiency;
- Provide laboratory sessions to implement productivity and efficiency analyses in practice;
- Provide the basic concepts to analyse the specialised literature;
- Interact with students through assisted laboratory and the realization of a practical work on real data, seminars and oral presentations.

SPECIFIC OBJECTIVES:
• knowledge and understanding: demonstrate the knowledge of the basic elements of productivity and efficiency analysis;
• ability to apply knowledge and understanding: to be able to apply efficiency analysis techniques learned during the course in its own engineering area of specialization;
• judgment autonomy: to be able to perform an efficiency analysis with critical spirit, choosing the appropriate method and correctly implementing it.
• communication skills: being able to communicate the results of the analysis and its information to different types of interlocutors;
• learning skills: to develop the necessary skills to apply and develop autonomously the methods learned during the course.

Educational objectives

Knowledge and understanding
Students will acquire advanced knowledge of financial market microstructure, algorithmic trading concepts, and the statistical foundations of quantitative strategies. They will thoroughly understand how to translate theoretical trading ideas into computational logic using programming languages such as Python and EasyLanguage.

2) Applying knowledge and understanding
Students will be able to apply theoretical concepts to practically build, test, and optimize automated trading systems. They will use Python for advanced data analysis, statistical modeling, and machine learning integration, and will employ EasyLanguage for rapid strategy prototyping, platform integration, and market execution.

3) Making judgements
Students are encouraged to critically discuss quantitative trading strategies, market anomalies, and algorithmic models in the classroom. The course includes practical sessions in which students apply the acquired knowledge to design, test, and evaluate empirical trading models. Using historical and live market data, students will critically assess the performance and robustness of their strategies implemented through EasyLanguage and Python, learning to distinguish between genuine predictive power and data overfitting.

4) Communication skills
At the end of the course, students will be able to illustrate and explain algorithmic logic, risk metrics (e.g., Sharpe ratio, maximum drawdown), and the strengths and weaknesses of various trading methodologies to a variety of heterogeneous interlocutors (such as investors, risk managers, or software developers). The acquisition of these skills is verified and evaluated not only during the final exam, but also during project presentations and assessments in which, individually or in groups, students are called to present their own trading algorithms and showcase comprehensive backtesting reports.

5) Learning skills
Students acquire the ability to independently research, design, and code quantitative trading systems using Python and EasyLanguage. The tools provided by the course allow for the analysis of complex financial markets, enabling students to build models that simultaneously account for multiple market factors, execution costs, and the strict risk management inherent in algorithmic trading.

Educational objectives

The course aims to describe the founding principles, the scope and the fundamental toolsand
methodologies of project management and of organizational processes.
Starting from the concept of integrated management of projects, all the
main methods for managing the performance variables of quality, time and
cost will be proposed. In line with the main standard processes of
project management the internationally standardized project management
terminology will be used. The course also intends to present the
principles and techniques of business process re-engineering and
management.Risultati di apprendimento attesi (Inglese):At the
end of the course the student will be able to plan a project starting
from the objectives of quality, time and cost defined by internal or
external customers of a company. She/he will also be able to critically
analyze an ongoing project proposing both organizational and managerial
improvements and both the use of correct project management
methodologies.

Educational objectives

The course gives on-field experience of Operations Management, from an operational and technical point of view. The course develops one of the main topics of Operations Management, e.g. company-market relations, supply chain management, production and inventory management.

EXPECTED LEARNING OUTCOMES. Knowledge and understanding: Deepening and consolidation of the knowledge provided in the Operations Management course. Capability: Expected learning outcomes include the ability to develop analyses, model problems, and identify the best techniques for solving major characteristic problems in production management, materials management, logistics management, and supply chain management through project work and on-field experience.

Educational objectives

The objective is to provide the management engineer with the basis for understanding the theory and principles of innovative optical, photothermal, photoacoustic, radiometric and thermographic techniques and instruments, and to understand the main applications together with their industrial relapses and with a deep analysis of the economical impacts and parameters.

The topics are organized to provide scientific technical expertise in order to work in the field of applied research, making easier the employment of the new engineer in industry.

Contents of the proposed course:

The course will provide the theoretical bases of the optical systems, the laser with its applications. Photothermal, photoacoustic, radiometric, and infrared techniques will be introduced for nonsdestructive evaluation and testing of materials, together with the many applications in different fields: industry, environment, energy, but also biology, medicine, and in the emerging sector of agri-food. Final comparisons will be introduced among the diagnostic techniques by using parameters of the management engeneering. The course contains also some experimental activities in laboratory and the data analysis and processing.

Educational objectives

Main course’s objective is
developing digital competences to make citizens able to use Information
and Communication Technologies (ICT) at work, in day-to-day relationships, in
dealing with public services as well as in culture, entertainment, leisure and
when participating in community and political dialogues. This course contributes
to actualize the EU i2010 program “increasing social
inclusion through reducing digital divide to build a society for
all”. The course promotes ICT use also to put in action the Italian Digital
Administration Low as well as the ICT right use (art.3) and the Public
Administration Data Availability (art 50 capo V).

Educational objectives

The specific goal of these activities is to enable the students to merge their academic knowledge with professional skills.

Educational objectives

The specific aim is to allow the student to use and expand the bulk of knowledge acquired during the course of study performing some activities in an industrial setting, a company, or a research laboratory.

Educational objectives

The course will provide the foundations of theoretical, technical and practical aspects in the design and
implementation of machine learning systems, in particular neural networks based on the use of innovative
technologies such as Quantum Computing and Hyperdimensional Computing/Vector Symbolic Architectures, in
connection with the most advanced Deep Learning methodologies. The focus will be on the study of such
computational approaches for applications in the field of industrial and information engineering for the solution of
supervised and unsupervised problems in particular concerning optimization, approximation, regression,
interpolation, prediction, filtering, pattern recognition and classification. The main goal is to provide the student with
the ability to understand how to obtain advantages both from the quantum point of view (quantum advantage) and
from the use of distributed representations. The systems thus developed can then be used in applications related to
data-driven learning problems, i.e. in time series analysis, hyperdimensional computing and eXplainable AI,
considering the different real-world domains related to energy, aerospace, Earth observation, behavioral analysis,
bioengineering, finance, security, fraud detection and so on.

The main objective of the course is to enable students to develop hybrid and innovative systems mainly based on
Quantum Deep Neural Networks, distributed representations and hyperdimensional computing through an adequate
formulation of the problem, an appropriate choice of algorithms suitable for solving the problem itself and the
execution of experiments in laboratory activities so as to evaluate the effectiveness of the adopted techniques. The
applications of such systems will therefore be explored in vertical case studies such as, but not limited to, the
management of complex networks (smart grids, energy and goods distribution, biological and sociological networks,

etc.), the analysis of materials, the design of devices and circuits, automation and control systems, the inversion of
physical models and abstract organizational and decision-making models, telemedicine and so on.

Through a systematic laboratory activity, during which the methodologies related to the design and implementation of
quantum and hyperdimensional computing architectures will be taken into consideration, the student will integrate the
acquired knowledge to manage the complexity of inductive learning mechanisms and the real limits imposed by the
Noisy Intermediate-Scale Quantum (NISQ) quantum devices currently adopted, also and above all in function of the
application domains in the field of Industrial and Information Engineering that will be considered in the course. Particular
emphasis will be given to the understanding of the use of symbolic and hyperdimensional computing (HDC/VSA) for
efficient data processing, highlighting how this approach can offer significant computational and interpretative advantages compared to traditional methods.

Quantum technologies and quantum algorithms for information processing are rapidly evolving, considering the current
scenario based on short-term devices and hybrid quantum-classical approaches. Similarly, hyperdimensional and
(neuro)symbolic computing is establishing itself as a relevant paradigm for numerous applications with a high innovative
and technological coefficient. At the end of the course, the student will be able to communicate the knowledge acquired
to specialist and non-specialist interlocutors in the research and work fields in which he/she will carry out the subsequent
scientific and/or professional activity, also taking into account technological and sustainable development issues.

The adopted teaching methodology includes an autonomous and self-managed study activity during the development
of single-subject tasks, in a vertical manner on some specific theoretical and applicative topics using, for instance,
quantum resources available in the cloud such as IBM's Quantum Experience Platform, as well as quantum simulators
such as Qiskit, Pennylane and Flax in a Python environment, in addition to specific development frameworks for HDC
(TorchHD), all for the creation of machine learning systems applied to Industrial and Information Engineering
problems in the management, electrical, mechanical, logistics, biomedical fields and for the training of professional
and business skills capable of relating in the technical-scientific context of data analytics and business intelligence.

Educational objectives

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General Objectives:
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Major advances in technology have resulted in the widespread implementation of information systems into businesses and organizations.

This course introduces languages, principles and methods of process modeling, analysis and innovation as critical factors to the
overall success of a business.

The course centers around the role of conceptual (sometimes referred as business) process modeling as a means to understand and capture the
workflows of interest in information systems of various kind. Students will learn the elements of process models and their precise meaning
using the Business Process Model and Notation (BPMN) international standard.

The course will cover processes within organizations (process orchestrations) and also interacting processes involving several organizations
(process choreographies), and will look at techniques to analyze and improve such processes from a formal perspective.

The course will also provide a basic knowledge and understanding of how to design, test and implement information systems for executable processes.

Finally, the course will present methods and tools to properly use process mining techniques, which enable to discover process models (whose structure
is unknown at the outset) starting from the logs recording the concrete events executed by the real workflows.

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Specific Objectives:
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Knowledge and understanding:
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At the end of the course, the students:
- learn the main methods to carry out a BPM (Business Process Management) project;
- are able to model a process with the BPMN standard;
- are able to implement and execute a process through a real information system;
- understand process mining algorithms and techniques.

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Applying knowledge and understanding:
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The students will be able to use suitable methodological and technological solutions for
(i) modeling a process in BPMN;
(ii) analysing it with quantitative techniques;
(iii) executing and monitoring it with an information system.

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Making judgements:
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The student acquires autonomy of judgment in proposing the most suitable approach to carry out a BPM project.

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Communication:
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The project activities and the lectures of the course allow the students to develop the proper abilities to communicate/share the design choices and
development methods for realizing any step of the business process life-cycle.

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Lifelong learning skills:
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In addition to the traditional learning skills provided by studying the teaching material, the project activities stimulate the student to
deepen her knowledge of the BPM topic, to improve the teamwork, and to the concrete application of the concepts and techniques investigated
during the course.

Educational objectives

General Objectives.
The Reinforcement Learning (RL) course aims to introduce students to fundamental and advanced techniques of RL, a significant area within artificial intelligence and machine learning. Students will gain skills to design and implement algorithms that enable systems to learn and improve autonomously through experience, optimizing their decisions in real-time.

Specific Objectives.
Students will explore key concepts of RL such as decision policies, Markov Decision Processes, Q-learning, and deep reinforcement learning. They will learn to:
Model complex problems using the RL approach.
Develop and implement algorithms like Q-learning and Deep Q-Networks (DQN).
Apply RL techniques in real-world scenarios like robotics, gaming, etc.

Knowledge and Understanding:
In-depth knowledge of basic and advanced RL algorithms.
Understanding of reward-based learning models and their practical applications.
Ability to interpret the results of RL algorithms and evaluate their effectiveness in various contexts.

Applying Knowledge and Understanding:
Use software frameworks like TensorFlow or PyTorch to implement and test RL algorithms.
Analyze current research case studies and projects to understand real-world RL applications.
Develop functional prototypes using RL to solve specific problems.

Autonomy of Judgment:
Students will develop the ability to critically assess RL algorithms, considering their applicability, efficiency, and potential biases. They will also be able to select the most appropriate algorithm for a given problem.

Communication Skills:
Students will learn to effectively communicate RL concepts, algorithm design decisions, and outcomes to both technical and non-technical audiences using a variety of communication media.

Next Study Abilities:
This course will prepare students to pursue advanced studies and research in RL, providing the necessary foundation to tackle open problems and innovate in the field. Students will be encouraged to actively contribute to the scientific community through publications, conferences, and collaborations.