| 1044398 | INTERACTIVE GRAPHICS [ING-INF/05] [ENG] | 1st | 2nd | 6 |
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.
|
| 1052229 | Computer Vision [ING-INF/05] [ENG] | 1st | 2nd | 6 |
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.
|
| 10606830 | Internet-of-Things Networks and Protocols [INF/01] [ENG] | 1st | 2nd | 6 |
Educational objectives The course will make students aware of the challenges behind the design, implementation and field use of Wireless system, sensing systems and the Internet of Things. The course will present both the theoretical foundations and practical aspects you need to know to develop such systems. Hands on lab experiences are associated to the course.
Part 1, Wireless networks
Fundamentals of wireless systems
Fundamental of ad hoc and cellular networks
Part 2, Internet of Things Core
Internet of Thigs applications, architectures, enabling technologies and protocols
Part 3, Emerging Technological Trends in Internet of Things
Zero power sensing systems: Wake Up Radio, energy harvesting, ...
ML based system optimization
Cyber physical systems for the Blue Economy
Part 4, From technologies to Applications
Internet of Things for smart planet and smart cities: practical examples of how to put the pieces together to implement real systems
Part 5 (Lab): Simulating, implementing and testing novel ideas on wireless networked systems and IoT systems
Performance evaluation of Internet of Things systems: How to model, what to model
Network simulators for Internet of Things
How to move from an idea to a validated idea to a solution
Lab: The course provides some lectures on C++ tailored to what needed to program simulators on Internet of Things systems.
For students with limited background on C/C++, recording of classes on C++ from previous courses will be shared so that you can get the needed background
|
| 10616534 | Information technologies for smart manufacturing [ING-INF/05] [ENG] | 1st | 1st | 6 |
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.
|
| 1027171 | NETWORK INFRASTRUCTURES [ING-INF/03] [ENG] | 1st | 1st | 6 |
Educational objectives General Objectives
The "Network Infrastructures" course provides an in-depth overview of the main architectures, protocols, and technologies of modern network infrastructures, with a particular focus on broadband access networks, optical transport networks, and next-generation wireless solutions. Students will gain a detailed understanding of the fundamental technologies and protocols for configuring and managing telecommunications networks, covering both theoretical and practical aspects. The course includes hands-on exercises on network configurations using advanced simulation tools, developing essential operational skills in the telecommunications sector. Additionally, key network security solutions and Quality of Service (QoS) support mechanisms will be analyzed, preparing students to understand and address emerging challenges in network infrastructures.
Specific Objectives
Knowledge and understanding: Students will acquire a deep understanding of network architectures, access technologies (xDSL, PON, LTE, 5G), transport protocols (OTN, MPLS), and routing mechanisms.
Applying knowledge and understanding: Students will be able to configure, analyze, and troubleshoot IP networks using simulation tools such as Kathara.
Autonomy of judgment: Students will develop the ability to critically evaluate different network technologies and select optimal solutions based on security, performance, and scalability requirements.
Communication skills: Students will be able to present network technology concepts clearly in both technical and general contexts.
Learning skills: The course will provide methodological foundations to keep up with the continuous evolution of network technologies and independently explore new solutions and emerging standards in the telecommunications sector.
|
| 1023235 | Robotics I [ING-INF/04] [ENG] | 1st | 1st | 6 |
Educational objectives General objectives
The course provides basic tools for the control of robotic systems: kinematic analysis, trajectory planning, 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.
|
| 10606936 | Programmable networks [ING-INF/03] [ENG] | 1st | 2nd | 6 |
Educational objectives General Objectives.
The course aims to provide students with an overview of network programmability, introducing the main architectures and enabling technologies. Through frontal teaching and practical exercises, students will be able to configure network devices, design and implement network management automation applications, develop control applications, and define new packet processing logics.
Specific Objectives.
Knowledge and understanding:
Understanding of the main architectures supporting programmable networks, including the functions performed by different logical blocks.
Application of knowledge and understanding:
Ability to design and develop network control applications, network automation, and packet processing pipelines.
Critical and judgmental abilities:
Ability to critically analyze the cost/benefit relationship regarding the use of centralized control architectures, reactive or proactive approaches, physical or virtualized network functions.
Communicative skills:
Through group activities carried out in the classroom and the completion of the exam project, students will acquire the ability to illustrate the logic of operation of the various developed network functions, as well as explain how these can integrate with various architectural elements.
Learning abilities:
The course provides students with a structured and systematic vision of the various points of programmability in a network infrastructure, as well as commonly used architectures. This knowledge will enable students to easily understand the role of network programmability even in application scenarios not covered in the course.
|
| 1052058 | Laboratory of Network Design and Configuration [ING-INF/03] [ITA] | 1st | 2nd | 6 |
Educational objectives GENERAL
The aim of the course is to provide a practical approach about the management of IP networks. The course will allow students to critically evaluate the main network protocols studied in previous courses (IP addressing, routing protocols, Ethernet, etc…) and it will describe advanced network solutions (NAT, Virtual LAN, Access Control List, etc…). A network emulator will be used to configure an IP network like in a real scenario, so that to implement the protocols studied; moreover, specific troubleshooting procedures will be described and tested.
SPECIFIC
• Knowledge and understanding: to know the main network protocols used in an IP network.
• Applying knowledge and understanding: to configure an IP network by means of a network emulator providing a configuration interface for IP routers and Ethernet switches.
• Making judgements: to carry out network design solutions as a function of specific network requirements.
• Communication skills: (none).
• Learning skills: ability to continue successive studies concerning with advanced networking.
|
| 1047220 | BIOINFORMATICS [ING-INF/06] [ENG] | 1st | 2nd | 6 |
Educational objectives General outcomes:
The course will focus on statistical and unsupervised data mining methods for medicine. Students will acquire basic biological knowledge, knowledge of major biological databases and data analysis tools, bioinformatics skills and familiarity with omics data analysis.
Specific outcomes:
Knowledge and understanding:
Students become familiar with basic biological concepts, R programming applied to bioinformatics, the analysis of gene expression data using statistical and unsupervised methods for the investigation of complex diseases.
Applying knowledge and understanding:
Students will be able to perform a standard bioinformatic analysis by applying the statistical techniques acquired during the course to identify modulated molecules potentially characterizing a disease phenotype.
Making judgements:
Students will be able to evaluate the quality of the performed data analysis, characterizing the results through the investigation tools presented during the course and seeking for literature-based evidence of the obtained results.
Communication skills:
The course includes practical sessions and a final project activity that will allow the student to be able to understand, present and adequately discuss the results obtained from a basic bioinformatics data analysis carried out on real case studies, as well as communicate and justify the methodological and parameter choices used to accomplish this analysis.
Learning skills
The course includes theoretical lessons that will allow the student to develop the usual learning skills from the theoretical study of the teaching material, and practical sessions, in particular project activities on real case studies of molecular data analysis relating to various pathologies, thus stimulating the student both to independently study some of the topics presented in the course and to concretely apply the notions and techniques learned during the course.
|
| 10600453 | Project management [ING-IND/35] [ENG] | 1st | 2nd | 6 |
Educational objectives GENERAL OBJECTIVES
The course clarifies and transfers to students the founding principles, the scope and the fundamental tools
and methodologies of Project Management (PM). 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. 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 or closed project proposing both organizational and managerial improvements and both the use of correct Project Management methodologies.
SPECIFIC OBJECTIVES
KNOWLEDGE AND UNDERSTANDING. The course will allow an in-depth comprehension of the fundamental concepts and tools of Project Management in the main application contexts: new product/service development, business process reengineering and management of engineering-to-order jobs . The students will learn to recognize and to master the best practices of Project Management and to apply them in real contexts.
CAPABILITY TO APPLY KNOWLEDGE AND UNDERSTANDING. Through this course students will be able to plan a project starting from the objectives of quality, time and cost requested by the internal or external client, to manage the project execution phase through a proper monitoring of the activities, and to assess project benefits in line with the expectations of the main stakeholders. They will also be able to critically analyze a project in progress or finished proposing both organizational and management improvements and the use of correct Project Management methodologies.
MAKING AUTONOMOUS JUDGEMENTS. After the course, students will be able to choose, for a given project, the best methodology through a deep understanding of the requirements and constraints of the business context; moreover they will develop the ability to critically analyze a project.
COMMUNICATE SKILLS. At the end of the course the students will be able to illustrate the concepts of Project Management using the standard international terminology, to organize information and project data according to a format and a standardized reporting process comprehensible to professionals, and to present in depth all the aspects of a project to an audience of specialists and non-specialists.
LEARNING SKILLS. The student will develop the capability to autonomously study, the capability of teamworking and the critical understanding and evaluation of projects and of different Project Management methodologies.
|
| 10616549 | Advanced cryptography [INF/01] [ENG] | 1st | 2nd | 6 |
Educational objectives General Objectives
Traditional cryptographic tools are insufficient for data protection
in emerging scenarios. The objectives of this course consist of
presenting several modern cryptographic tools and techniques along
with their applications to realize the principle of "security and
privacy by design" in the Cyberspace. This course provides both
theoretical and practical expertise.
Specific Objectives
The course will illustrate the power of advanced signature schemes,
advanced encryption schemes, verifiable random functions,
privacy-preserving proof systems and cryptographic puzzles. A
particular focus will be given to concrete applications like e-voting,
e-auction, privacy-preserving contact tracing, digital cash, anonymous
cryptocurrencies, identity wallet, secure messaging, fighting
misinformation, GDPR compliance (right to be forgotten and data
minimization principles), practical libraries and tools for advanced
cryptography.
Knowledge and Understanding:
-) Knowledge of the security properties of advanced cryptographic tools.
-) Knowledge of the main hardness assumptions, on which the security
of advanced cryptographic tools is based.
-) Knowledge of the cryptographic schemes currently used in real life.
-) Understanding of their (practical and theoretical) properties.
Applying knowledge and understanding:
-) How to select and combine together the right advanced cryptographic
tools for a given application.
-) How to analyze the security and efficiency of a system based on
advanced cryptographic tools.
Critiquing and judgmental skills:
The students will be able to judge whether a system is secure or not
according to a realistic threat model.
Communication Skills:
The students will learn how to illustrate the resilience of a digital
system to concrete attacks.
Ability of learning:
The students will obtain the necessary background for a deeper study
of the subjects.
|
| 1022870 | NEURAL NETWORKS [ING-IND/31] [ENG] | 1st | 1st | 6 |
Educational objectives General objectives:
The course is intended as a broad overview to neural networks, as used today in a number of applicative fields. It provides a strong theoretical and practical understanding of how neural networks and modern deep networks are designed and implemented, highlighting the most common components, ideas, and current limitations.
Specific objectives:
From a theoretical point of view, we will review the general paradigm of building differentiable models that can be optimized end-to-end with gradient descent from data. We will then overview essential components to design architectures able to work on images (convolutive layers), sequences (recurrent layers), and sets (transformer layers). The last part of the course will then focus on a selection of important research topics, including graph neural networks, continual learning, and generative models.
Knowledge and understanding:
At the end of the course, the student will have a broad understanding of how deep networks work in practice, with the capability of implementing new components from scratch, re-using existing models, or designing new architectures for problems beyond the overview of the course.
Critical and judgment skills:
The student is expected to be able to analyze a new problem requiring machine learning, and design the appropriate neural network based solution to tackle it, understanding both its strengths and its drawbacks.
Communication skills:
The course will foster communication skills in terms of being able to describe (in both a technical and non-technical way) the mathematics underlying the models, as long as writing clear and understandable code for its implementation.
Learning ability:
Beyond the topics of the course, the student will be able to autonomously study new topics on the research frontier, and navigate the current scientific literature and software panorama.
|
| 10616576 | Innovation Management [ING-IND/35] [ITA] | 1st | 2nd | 6 |
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
|
