Educational objectives Learning goals:
Eliminate possible gaps in the basic mathematical knowledge normally expected from students with upper secondary school diplomas and to establish a uniform language and a basic preparation so that they can follow the curriculum courses more beneficially.
Knowledge and understanding:
Good theoretical and practical knowledge of basic mathematical notions normally included in upper secondary school programs and ability to understand these issues also in the context of institutional courses.
Applying knowledge and understanding:
Ability to use the acquired skills for solving simple problems on the basic mathematical notions normally included in upper secondary school programs also for applications required in institutional courses.
Making judgements:
Good ability to recognise, frame and set out the resolution of simple problems on the basic mathematical notions normally included in upper secondary school programs, possibly selecting appropriately among the methods learned.
Communication skills:
Good presentation skills of basic concepts and techniques normally included in upper secondary school programs as well as solution methods to simple problems.
Learning skills:
Good learning ability of mathematical issues in institutional courses, by virtue of the comprehension of the logical-deductive character of the discipline.
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Educational objectives The goal is to provide students with the so-called soft skills, useful for future academic and professional activities.
They include: public speaking, ability in producing scintific written reports, use of scientific text editors (e.g. Latex), team working, etc.
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Educational objectives Learning goals
The course aims to provide the main modeling techniques used for the optimization problems in Operations Research and the basic elements of the packages for the solution of these problems.
Knowledge and understanding.
After attending the course the students know and understand the main classes of problems in Linear Programming and Integer Linear Programming and the basic elements of the packages for Mathematical Programming.
Applying knowledge and understanding.
At the end of the course the students are able to analyze simple real problems, write the appropriate Linear Programming or Integer Linear Programming model, find an optimal solution with a package for Mathematical Programming and analyze it.
Making judgements.
Students learn to recognize and modelling different classes of optimization problems, and to do a post-optimal analysis of the obtained results.
Communication skills.
During the lessons the students acquire the basic elements of the language of the discipline also thanks to the direct interaction with the teacher.
Learning skills.
After the exam the students have learned to model and solve using a package some classes of optimization problems and are able to deepen their knowledge and skills in more complex modeling contexts.
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Educational objectives Educational Objectives
The course "AI Laboratory" aims to provide students with a practical understanding of the main tools and techniques in artificial intelligence, favoring a statistical-probabilistic approach where appropriate. Through hands-on lab activities and applied projects, the course seeks to develop operational skills in the use of machine learning algorithms, deep neural networks, and other emerging AI technologies such as LLMs and diffusion models. Students will be guided in designing and implementing solutions to real-world, multidisciplinary problems, fostering critical thinking and a deeper awareness of both the potential and the limitations of the studied tools.
Knowledge and Understanding
The course offers students a solid practical understanding of the main tools and techniques in artificial intelligence, with particular attention to statistical-probabilistic approaches. It promotes knowledge of machine learning algorithms, deep neural networks, advanced generative models (such as LLMs and diffusion models), and their applications.
Ability to Apply Knowledge and Understanding
Through laboratory activities and applied projects, students learn to concretely apply the techniques studied to develop solutions for real and multidisciplinary problems, gaining operational skills in the use of advanced AI tools.
Independent Judgment
The course encourages the development of critical thinking and awareness of the strengths and limitations of the tools used, promoting an independent and reflective evaluation of the solutions adopted in various application contexts.
Communication Skills
Participation in multidisciplinary applied projects requires the ability to effectively communicate results, design choices, and challenges, even in collaborative contexts, thus contributing to the development of technical communication skills.
Learning Skills
The practical and lab-based nature of the course fosters active learning, adaptability to new technologies, and the ability to stay up to date in a rapidly evolving field like artificial intelligence.
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Educational objectives Educational Objectives
The course "AI Laboratory" aims to provide students with a practical understanding of the main tools and techniques in artificial intelligence, favoring a statistical-probabilistic approach where appropriate. Through hands-on lab activities and applied projects, the course seeks to develop operational skills in the use of machine learning algorithms, deep neural networks, and other emerging AI technologies such as LLMs and diffusion models. Students will be guided in designing and implementing solutions to real-world, multidisciplinary problems, fostering critical thinking and a deeper awareness of both the potential and the limitations of the studied tools.
Knowledge and Understanding
The course offers students a solid practical understanding of the main tools and techniques in artificial intelligence, with particular attention to statistical-probabilistic approaches. It promotes knowledge of machine learning algorithms, deep neural networks, advanced generative models (such as LLMs and diffusion models), and their applications.
Ability to Apply Knowledge and Understanding
Through laboratory activities and applied projects, students learn to concretely apply the techniques studied to develop solutions for real and multidisciplinary problems, gaining operational skills in the use of advanced AI tools.
Independent Judgment
The course encourages the development of critical thinking and awareness of the strengths and limitations of the tools used, promoting an independent and reflective evaluation of the solutions adopted in various application contexts.
Communication Skills
Participation in multidisciplinary applied projects requires the ability to effectively communicate results, design choices, and challenges, even in collaborative contexts, thus contributing to the development of technical communication skills.
Learning Skills
The practical and lab-based nature of the course fosters active learning, adaptability to new technologies, and the ability to stay up to date in a rapidly evolving field like artificial intelligence.
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