Section outline

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    General

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     At the moment one can access this module only as a guest, self enrollment will be activated in January 2027. 

    Information on services and SW available to UNIGE students can be found on CEDIA, while for TEAMS and Office365 you should read didattica-a-distanza.

    To get internet access within UNIGE (and other univerisities in the world) you should use the wifi network eduroam, which requires UNIGEPASS credentials (as alternative one may use GenuaWifi). Instructions (in Italian) on how to configure on your PC for accessing eduroam are on the web page https://cedia.unige.it/wifi-istruzioniconfigurazione.

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    Mini-courses selection

    For each mini-course there is a section. The choices below allow you to select which mini-course sections to view (you can always modify your choices).

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    Algorithmic Problem Solving (Matteo Dell'Amico)

    • Aims. Apply your knowledge of algorithms and data structures to find efficient solutions to programming problems. The course will be largely hands-on.
    • Prerequisites. Some previous experience with programming, data structures, and algorithms.
    • Exam. A lab test consisting of problems to solve within a limited amount of time.
    • Student assistance hours: after lectures, or appointments set up via email (matteo.dellamico@unige.it).
    • APS News Forum

      Announcements and news for this mini-course

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    Algorithmic Techniques in Python (Moggi) - 20 hours (2h per week)

    • Aims. Review some well-established algorithmic techniques for problem solving by exploiting the features offered by Python.
    • Prerequisites. Basic knowledge of Algorithms and Data Structures and some high-level programming language is essential.
    • Exam. A lab test consisting of programming exercises done individually in DIBRIS SW labs and submitted on aulaweb.
    • Student assistance hours (by Eugenio Moggi): at the end of lectures or on teams (TIME to be agreed).
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    Computational Imaging & Learning (Calatroni) - 20 hours

    • Premise. This course provides an introduction to computational imaging, from mathematical/physical models of image formation to modern reconstruction methods combining model-based optimization and data-driven learning. We will discuss ill-posedness, variational and Bayesian regularization, first-order optimization algorithms, and learning-based approaches such as algorithm unrolling. Particular attention will be given to how physical models, mathematical structure, and learned components can be integrated to obtain accurate, efficient, and interpretable imaging methods, with examples and hands-on notebooks illustrating the theory in practice.
    • Aims. The course aims to provide the mathematical and computational foundations for formulating and solving imaging inverse problems using model-based, learning-based, and hybrid approaches.
    • Exam. Oral exam consisting of a discussion of a computational project previously agreed with the teacher and questions on the material covered in the course.
    • Student assistance hours: please contact the instructor (luca.calatroni@unige.it).
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    Geometric Vision: from images to 3d (Odone) - 20 hours

    • Premise.  3D computer vision is a branch of computer vision devoted specifically to the extraction of 3D information from images and videos. 
    • Aims. The course aims to provide the basics of 3D computer vision: we will start from an introduction to digital images and image formation geometric models and we will explore three main directions: planar scenes, stere-systems, and pose estimation 
    • Exam. Oral exam consisting of a discussion of a computational project previously agreed with the teacher and questions on the material covered in the course.
    • Student assistance hours: please contact the instructor  francesca.odone@unige.it 
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    Introduction to Category Theory (Moggi) - 20 hours (2h per week)

    • Premise. Category theory is a young branch of mathematics. Born officially in 1945 from a paper by Samuel Eilenberg and Saunders Mac Lane, it soon found important applications in algebra, geometry and logic thanks to Alexander Grothendieck and William Lawvere. Category theory has been expanding its areas of application by including also physics and computer science.
    • Aims. Present the fundamental concepts in Category Theory and exemplify its use in mathematics and computer science.
    • Prerequisites. Basic knowledge of Algebra and Logic is essential. Basic knowledge of Linear Algebra and Calculus is desirable.
    • Exam. Oral exam consisting of a discussion of a paper previously agreed with the teacher, or a written exam with exercises on the material covered in the course.
    • Student assistance hours (by Eugenio Moggi): at the end of lectures or on teams (TIME to be agreed).
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    Software engineering for ML(AI)-fueled systems (Reggio, 20 hours)

    • Premise.  Engineering systems fueled by ML(AI) pose new problems, such as managing data as first-class citizens, building and monitoring ML models, guaranteeing the responsible use of ML(AI), and thus require specialised practices.
    • Aims. The course first introduces the novelties of engineering ML(AI)-fueled systems. Second, it provides a selected set of reusable conceptual resources (e.g., design and architectural patterns and anti-patterns, reference architectures, best practices, guidelines, and smells) to support development.
    • Exam. Oral exam consisting of a discussion of the results of the hands-on activities and of the material covered in the course.
    • Student assistance hours: please contact the instructor: by email gianna.reggio@unige.it or by Teams