Engineering in Computer Science

Educational objectives

General outcomes:

The objective of the course is to study the most important quality of software: correctness. Such a study concerns bot the static aspects (data) and the dynamic aspects (processes) of software, considering both how to conceptualize and model such aspects and how to verify them. The main tools used for such study are various forms of logic: first-order logic and description logics for the static aspects, Hoare Logic and dynamic and temporal logics of programs for the dynamic aspects. After a successful completion of the course, the student will have acquired techniques and methods to model and verify programs, both wrt data and processes.

Specific outcomes:

Knowledge and understanding:
Learn the fundamentals of formal methods. The use of strict and formal specifications and their verification. Founding principles of logic in computer science logic and formal verification of data and processes.

Applying knowledge and understanding:
Being able to apply the acquired knowledge to perform analysis of the correctness of programs through rigorous and formal methods, both in relation to aspects relating to data and processes.

Making judgements:
Being able to evaluate the rigor of a given argument of correctness of the programs. Being able to choose the conceptual tools provided by logic and formal methods for the verification of both static and dynamic properties.

Communication:
The group activities in the classroom as well as group projects make the students able to communicate / share the acquired knowledge and to compare himself with others on the topics of the course.

Lifelong learning skills:
In addition to the competences provided by the study of the teaching material, the course teaches the students to deepen their knowledge of some of topics presented in the course, while working in a group, and concretely apply the concepts and techniques learned to specific case studies.

Educational objectives

General Objectives:
The main goal of this course is to introduce the fundamentals of human-computer interaction and to discuss in detail the concept of usability for interactive systems as well as the user-centered design (UCD). The various topics will be examined under different perspectives, dealing with theoretical, methodological, technological and application-oriented aspects, looking at them both in the current scenario and in view of future developments. Along the course, the student should acquire theoretical skills, methodologies, and techniques to be applied in a concrete project to be developed following the user-centered design.

Specific objectives:

Knowledge and understanding:
User-centered design. Techniques for requirement collection and analysis, goal and task models, interaction and system models, methods for usability evaluation. Some advanced issues in human-computer interaction, such as cooperative systems, immersive and ubiquitous environments, intelligent interfaces, etc.

Apply knowledge and understanding:
Understanding the concepts of human-computer interaction (or human-system interaction) and usability. Be able to conduct a complete project of an interactive interactive system following the UCD methodology.

Critical and judgment skills:
Being able to evaluate the usability of an interactive system and its adequacy with respect to the goals and tasks of end users and stakeholders.

Communication skills:
The project activities and the course exercises allow the student to be able to communicate / share the requirements of an interactive system, as well as the design choices and the development methods.

Learning ability:
In addition to the classic learning skills provided by the theoretical study of the recommended material, the course structure, in particular the project activities, stimulates the students to deepen their knowledge of the topics, working in team, and to practically apply the concepts and techniques learned during the course.

Educational objectives

General objectives:

The main goal of the course is to provide students with a predominantly technological background the main tools for designing a digital entrepreneurial activity

Specific objectives

The course provides students with the main complementary skills to develop a digital entrepreneurship project and it is made of four main sections:
1) Training aimed at acquiring lean methodologies and techniques for designing. The training will be inspired by the concepts of design thinking with the aim of clarifying and evaluating the technical feasibility of the project, sustainability in terms of business and the ability to satisfy the needs of a user (i.e. desireability)
2) How to present the project. The pitch
3) Best-practices. Successful experiences presented by entrepreneurs and / or researchers
4) Project activities in which students will test the skills acquired in the course in the design of a digital business activity.

Knowledge and understanding:

At the end of the course the student will know the main techniques, processes and methodologies to limit the risks associated with starting a digital entrepreneurship project.

Apply knowledge and understanding:

The course is characterized by an experimental "learn by doing" approach inspired by modern theories of design thinking. The skills acquired will be demonstrated in the realization of the final project.

Critical and judgment skills:

Critical and judgmental skills will be mainly developed through the project activity and the permanent discussion within the group, between the project groups and with the instructors. The lean approach will force students to perform a continuous critical exercise with the aim of better understanding the strengths and weaknesses of the proposed solution.

Communication skills:

Students will be able to present the achieved results through the presentation of a Pitch in a concise but effective way

Learning ability:

The course aims to change the mentality of the students so that the need to deal with the outside world in a structured way, not simply focusing on technological aspects, becomes a custom capable of projecting them with greater awareness in entrepreneurial activities.

Educational objectives

The course has the following educational objectives:

Knowledge and understanding.

The course aims to provide the knowledge necessary for understanding: (i) the specificities of mobile apps compared to desktop apps; (ii) the main design patterns for mobile apps; (iii) the main security issues; (iv) the use of the main backend cloud services for mobile applications; (v) the design and development methods of simple backend services deployed on the cloud; (vi) the classification of cloud service models

Ability to apply knowledge and understanding.

The student must be able to design, develop and test native applications for android operating systems that interact with cloud services using the main official development, test and design tools. The student must also be able to design / develop and test their own simple services deployed on cloud platforms, to support mobile applications

Autonomy of judgment.

Based on the skills acquired, the student must be able to evaluate the advantages of the disadvantages of the technologies with which it is possible to develop apps (native, hybrid and web based applications), evaluate / choose in an optimal and critical way the cloud support functionalities for the operation of mobile applications; to judge the feasibility, complexity and implications of new possible applications, also indicated by third parties. In addition, it must be able to update itself based on possible future technologies related to mobile apps or cloud services.

Communication skills.

The student must be able to motivate the technological, methodological and architectural choices to other people in the sector, as well as to present, even to inexperienced people, the operation and characteristics of possible new applications

Learning ability.

To stimulate the ability to learn, practical exercises will be carried out on the various topics covered and will be required to critically use information available for specific problems on various discussion platforms (eg Stack Overflow, official sites, blogs, etc.)

Educational objectives

The goal of the course is to provide an introduction to the currently used Information Visualization and Visual Analytics techniques. In particular, the course will analyze the methodologies for displaying purely numerical data (tables, diagrams) and representation techniques (mapping of attributes of the represented dataset in visual attributes), providing the practical skills to implement them in d3.js and integrate them with algorithmic solutions. Then, dimensionality reduction techniques will be introduced, with particular attention to PCA, MDS and t-SNE, presenting practical solutions in Python. Finally, the problem of presenting the described techniques will be introduced, acquiring skills on how to overcome limits of space and time in the visualizations, and providing indications on the main interaction techniques.

Educational objectives

The goal of the course is twofold. First, it will present the main theory behind the analysis of data. Second, it will be hands-on and at the end students will become familiar with various state-of-the-art tools and techniques for analyzing data. We will use Python for downloading data as well as its rich machine-learning libraries, the R environment for statistical processing, and the MapReduce framework for mining of large-scale data.

Educational objectives

General objectives:

To know the main languages of the current semantic technologies, in particular, the families of class-based and rule-based knowledge representaton formalisms, and the main reasoning techniques for such formalisms. To know the standard semantic technologies based on the above knowledge representation formalisms, in particular the RDF language and the OWL language, with the goal of designing and managing an ontological knowledge base. To know the basic elements of the representation of actions and reasoning about actions.

Specific objectives:

Knowledge and understanding:
Description Logics (the main class-based knoeledge representation formalisms) and the main rule-based languages, in particular Datalog and some of its extensions. The main Web standards for semantic technologies, in particular the RDF, SPARQL and OWL languages.

Applying knowledge and understanding:
To be able to design a knowledge base, choosing the most appropriate formalism and technologies for the given application context.

Making judgements:
To be able to evaluate the main semantic aspects of a knowledge base and of a knowledge-based application. To be able to choose the best available technology for processing a knowledge base.

Communication skills:
The practical activities and the exercises allow the student to be able to communicate and share the requirements of an application requiring the construction and management of a knowledge base and/or the usage of the standard semantic technologies.

Learning skills:
Besides the classical learning skills provided by the theoretical study of the teaching materials, the practical activities stimulate the student to autonomously deepen her/his knowledge about some of the course topics, to teamwork, and to the practical application of the notions and techniques learned during the course.

Educational objectives

General goals:
The goal of the course is to make students familiar with the basic
concepts of managing information systems at large scale. Two specific
topics will be investigated in detail, namely information models for Big
Data Management, and information integration. Both topics are extremely
relevant in the data-driven society, where virtually all information
systems of reasonably sized organisations need to both manage large data
sets, and to interact with several data sources.

Specific goals:
To study the data models used in Big Data Management, especially NoSQL
data models, including graph, XML, column-based, key-vale, and document
data models, and to get familiar with the notions and the techniques for
information integration.

Knowledge and understanding:
After the course the student will have a good knowledge on the
differences and similarities between the relational model and the
various classes of NoSQL data models. Moreover, the students will
understand the theoretical issues in data integration and exchange, and
will have a good knowledge about the various architectures of
information integration systems.

Apply knowledge and understanding:
The students will be able to design her/his own Big Data repository
using one of the data modelsadopted in practice, to choose an
appropriate architecture for information integration, and to build and
maintain an information integration systems structured according to the
chosen architecture.

Critical and judgment skills:
The student will be able to evaluate the requirement for a Big Data
Management system, and will be able to choose the right data model and
infrastructure to choose. Analogously, the student will be able to
understand the requirement for a specific information integration
system, and choose the appropriate approaches and techniques for
designing a high-quality solution.

Communication skills:
The students will acquire a good knowledge on how to illustrate the
results of a design process, both in the context of Big Data Management,
and in the context of information integration systems.

Learning ability:
The student will be able to understand any new architecture and approach
to Big Data Management and to Information Integration that will become
popular in the future

Educational objectives

General Objectives:
Knowledge of main application scenarios in Big Data Computing.
Knowledge and understanding of main algorithms and approaches in Big Data Computing. Knowledge of
main tools to implement them.
Understanding of theoretical foundations underlying main techniques of analysis
Ability to implement the aforementioned algorithms, approaches and techniques and to
apply them to specific problems and scenarios.
Knowledge of main evaluation techniques and their application to practical scenarios.

Specific objectives:
Ability to:
- identify the most suitable techniques to address a data analysis problem where
data dimensionality is concern;
- 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 needs into specific data analysis
problems;
- being able to identify aspects of the problem for which data dimensionality
might play a critical role;
- being able to identify the most suitable techniques and tools to addresse 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 andvanced manuals and/or scientific
literature, allowing them to face new application scenarios and/or apply alternative
techniques to known ones.

Educational objectives

Knowledge and understanding:

Have the student acquire the basics of 3D graphic programming with particular emphasis on animation and interactive visualization techniques. In particular the topics covered include: Fundamentals of computer graphics, interactive rendering and animation, graphics pipeline, transformations, visualizations, rasterization, lighting and shading, texture-mapping, animation techniques based on keyframes, physical simulations, particle systems and animation of characters. An introduction to computing on specialized graphics hardware (GPGU) will also be provided.

Applying knowledge and understanding:

To make the student familiar with the mathematical techniques underlying 3D graphics, as well as the ability to program complex and interactive environments in 3D graphics using the OpenGL library or one of its variants

Making judgements:

Deep understanding of the operation of a 3D graphics system in its hardware and software components. Knowledge of the HTML5 standard and the Javascript language, application of the WebGL library and some higher level libraries. Understanding of the problems of efficiency and visual quality of 3D graphics applications

Communication skills:

Development of interactive applications on the web in 3D graphics.

Learning skills:

Ability to understand the technical complexities in the realization of interactive applications in 3D graphics. Ability to critically analyze the solutions on the market and analyze strengths and weaknesses.

Educational objectives

This course introduces the main ideas of automated planning and mechanism for
formal logic reasoning within the field of artificial intelligence. The aim of
the sources is to prepare the student so that they can use the existing systems
for automated planning and understand their inner workings, which is
fundamental to adapt them to cope with issues arising from specific problems.
Furthermore, the student will understand the theoretical bases of the uses of
formal logics in artificial intelligence.

Educational objectives

GENERAL OBJECTIVES
The course aims to introduce the student to the fundamental concepts of artificial vision and to the construction of autonomous systems of interpretation and reconstruction of a scene through images and video. The course deals with basic elements of projective and epipolar geometry, methods for 3D vision and vision based on multiple views, and methods for metric reconstruction and image and video interpretation methods. Furthermore the course illustrates the main techniques for the recognition and segmentation of images and videos based on machine learning.

SPECIFIC OBJECTIVES

Knowledge and Understanding
The course stimulates students' curiosity towards new methodologies for the analysis and generation of images
and video. The student learns new concepts that allow him to acquire a basic knowledge of
computational vision.

Apply Knowledge and Understanding
Students deepen and learn programming languages ??to apply the acquired knowledge.
In particular they deepen the Python language and learn Tensorflow. The latter offers students
the possibility of programming deep learning applications. They use this brand new technology to make
a project to recognize specific elements in images and videos.

Critical and Judgment skills
The student acquires the ability to distinguish between what he can achieve with the tools he/she has learned,
such as generating images or recognizing objects using deep learning techniques,
and what is actually required for the realization of an automatic vision system.
In this way she/he is able to elaborate a critical judgment on the vision systems available to the state
of art and to assess what can actually be achieved and what requires further progress
in research.

Communication skills
The realization of the project, as part of the exam program, requires the student to work and give a
contribution within a small work group. This together with the solution of exercises in the classroom,
and to the discussions on the most interesting topics it stimulates the student's communication skills.

Learning ability
In addition to the classic learning skills provided by the theoretical study of the teaching material,
the course development methods, in particular the project activities, stimulate the student
to the self-study of some topics presented in the course, to group work, and to the application
concrete knowledge and techniques learned during the course.

Educational objectives

General Objectives:
Acquiring knowledge on the basic tools for probabilistic state estimation in robotics.
Being able to apply these tools to real study cases and to implement working solutions.
Evaluate the quality of a state estimator.

Specific Objectives:

Knowledge and Understanding:
- how to manipulate probability distributions, in particular Gaussians
- the basics of filtering (hisrogram filters, Gaussian filters, particle filters)
- the generic model for a stationary non-linear or linear
- Dense and Sparse formulation of minimization algorithms (Gauss-Newton, Levenberg Marquardt)
- The problem of Data Association, and typical tools to approach it (RANSAC, Heuristics)
- Typical study cases of estimation problems in robotics (Calibration, Localization, Mapping and SLAM)

Applying Knowledge and Understanding:
- Being able to model a problem and to adapt the tools to its solution.
- Develop a functioning estimator.

Making Judgements:
- Being able to analyze the pros and contra of different solutions to the same problem.
- Spot the tools applicable to solve all subtasks in the design of an estimator.
These abilities are supported by the Project to be developed as a part of the exam.
The course interleaves theory and practice. During the practicals the students are asked to
complete code snippets provided by the teacher and to run their programs on real study cases.

Communication Skills:
- Acquire a common language to describe estimators and a development methodology
that supports interaction between developers by defining a standard set of goals.

Learning Skills:
The student will possess the abilities and the skills to approach general estimation problems.
The examples in the domain of navigation provided during the course serve as study cases.
The indivudal topics learned (Gaussian Manipulation, Filtering Designs, Minimization)
are useful instruments to approach a far more general class of problems

Educational objectives

This course introduces principles and methods of process and
service modeling and analysis. The course emphasize the role of
conceptual (sometimes referred as business) process models as an
instrument to understand and capture the process or service of
interest in information systems of various nature. If focusses on
executable models, i.e., models that are formal enough to define what
it means to execute them and to formally verifying their dynamic
properties. Compositions and orchestration of available process and services
is also introduced.

Educational objectives

Knowledge and understanding

The main objective of the course is to provide an introduction to all the issues related to the security governance.
In particular, the student will learn how building up a security governance environment is a vertical problem with respect to the organisation and that its management impacts different enterprise's levels.
Aspects related to laws, regulations and both international and national standards will be analysed. It will then be discussed how, from a methodological point of view, these aspects are transposed and implemented through the definition of appropriate frameworks for cybersecurity management.

- Apply knowledge and understanding

Another fundamental aspect of the course is to provide students with methodologies and tools to let them able to face open problems with respect to the analysis, verification and certification of cybersecurity.

- Critical and judgment skills

The student will acquire the necessary tools to analyse, evaluate and compare different situations and design the appropriate countermeasures to improve the security status of the considered enterprise.

- Communication skills

The student will learn the domain specific language.

- Learning ability

The student will be able to adopt and re-apply all the methods discussed during the course

Educational objectives

The goal of the course is to present concepts for the study of social networks and online markets. We will use theoretical ideas from algorithms, statistics, economics, and sociology to study the structure of complex network and to provide models that allow us to understand how entities interact with each other and phenomena that arise from such interactions.

Educational objectives

General Objectives:
The course presents the basic concepts, protocols and architectures of current network infrastructures. Particular attention is paid to the broadband access network and new generation wireless networks. Technologies such as xDSL, PON, LTE, 5G, SDH, OTN, SDN will be discussed. Furthermore, IP and related protocols will be presented and implemented at various layers (at the network and at the application one) by adopting the Netkit platform. On Netkit the course will mainly focus on: dynamic routing, DNS, SSH, VPNs, Firewalls and security aspects.

Specific objectives:

A) Knowledge and understanding
- Knowledge of the main network infrastructures both wired (in copper and fiber) and in wireless. Ability to comply with the requirements and planning of next generation networks.

B) Apply knowledge and understanding
- Be able to identify problems related to digital network infrastructures, and analyze their requirements, in different application contexts (broadband, 5G, SDN and IoT).

C) Making judgements
- Being able, by integrating the skills at the network and application level, to understand the complexity of a network infrastructure and the type of service it can offer; be able to analyse the behavior of the procedures of different network protocols.

D) Communication skills
- The activities in the course projects and in the laboratory will allow the student to present, in different application contexts, the potential, limits and performance of network platforms.

E) Learning skills
- The design activities and the netkit laboratory stimulate the student to deepen his knowledge of key topics presented in the course; the work done in groups and the concrete application of the notions learned during the course will make the student able to interact with specialists and not specialists in the networking framework for the future digital infrastructures.

Educational objectives

This course introduces the neural networks (NN) paradigms, in its various aspects and exceptions, and some others soft computing (SC) methods which, unlike hard computing, are tolerant of imprecision, uncertainty and partial truth. The educational objectives is the acquisition of the following knowledge and skills relating to: 1) NNs and (also) non bio-inspired learning models: architectures, mathematical and statistical property, learning algorithms; 2) adaptive filtering and modelling of dynamic and memoryless phenomena; 3) parsimonious data representation and non-redundant information extraction; 4) architecture and learning of deep NNs with strong regularization methods; 5) algorithms for SC methods. Application on analysis of non-structured data: information retrieval; smoothing, modelling and prediction; patterns recognition; clustering; multi-sensors data fusion, blind source separation.

The student of course Neural Networks acquires basic and specific knowledge and skills related to the discipline. It is able to implement (and evaluate the performance) of various NN models, and their supervised or unsupervised learning algorithms for: feature extraction, pattern recognition, clustering and dynamic data processing. The student acquires a clear and effective understanding of the bio-inspired learning methods. It develops the ability to design adaptive systems that learn from real-world data with one or more of the following characteristics: non-structured data, noisy, incomplete, heterogeneous, high-dimensional, distributed.

Educational objectives

GENERAL OBJECTIVES
The course provides students with the basic principles and tools useful for Marketing and Innovation Management. Specifically, the course aims at teaching students: the main forces of the marketing environment; the management of marketing information to gain customer insights; consumer and business buyer behaviors; steps and tools to define and implement a marketing strategy; sources, types and patterns of innovation; standards battles and design dominance; the timing of entry; the mechanisms to protect innovation; the new product development process; the integration of environmental sustainability into marketing strategy and new product development. Further, through the analysis of several case studies, the course aims at stimulating analytical skills, which will allow students to understand and explain firm behavior and the related market results in the domain of marketing and technological innovation strategies, by applying principles and tools learnt during the course.

SPECIFIC OBJECTIVES
KNOWLEDGE AND UNDERSTANDING. The course will allow a comprehension of the fundamental concepts and tools of Marketing and Innovation Management. The students will learn to recognize and to master the best practices and success factors of Marketing and Innovation Management and to apply them in real contexts.

CAPABILITY TO APPLY KNOWLEDGE AND UNDERSTANDING. Through the course students will be able to develop a marketing plan, critically evaluate marketing and technological innovation strategies of firms, classify products based on their environmental impact.

MAKING AUTONOMOUS JUDGEMENTS. After the course, the student will be able to choose, given the main environmental forces, firm and innovation characteristics, the best marketing and technological innovation strategies. In addition, the student will develop the critical analysis capacity of marketing and innovation management.

COMMUNICATION SKILLS. At the end of the course the students will be able to illustrate the concepts of marketing and innovation management using internationally consolidated terminology and models, to organize information and data according to a format and a reporting process comprehensible to professionals.

LEARNING SKILLS. The student will develop the capability to autonomously study and critically understand and evaluate marketing and technological innovation strategies and related tools.

Educational objectives

General objectives

The course provides the basic tools for the kinematic analysis, trajectory planning, and programming of motion tasks for robot manipulators in industrial and service environments.

Specific objectives

Knowledge and understanding:
Students will learn how actuation units and sensing components of robots operate, the basic methods for the kinematic modeling, analysis and control of robot manipulators, as well as the main algorithms for trajectory planning.

Apply knowledge and understanding:
Students will be able to analyze the kinematic structures of industrial robots and to design algorithms and modules for planning and controlling robot trajectories.

Critical and judgment skills:
Students will be able to characterize the functionality of a robotic system with reference to a given industrial or service task, analyzing the complexity of the solution, its performance, and the possible weaknesses.

Communication skills:
The course will allow students to be able to present the main problems and the technical solutions related to the use and application of robotic systems.

Learning ability:
The course aims at developing autonomous learning abilities in the students, oriented to the analysis and solution of problems in the use of robots.