Diritti reali

Faculty of Communication Sciences, Faculty of Economics, Faculty of Informatics

Structure and contents

Artificial Intelligence may not only be the most exciting field in computer science, but of science in general. In fact, the best scientists of the future might even be AIs themselves. Hardware soon will have more raw computational power (CP) than human brains, since CP per cent is still growing by a factor of 100-1000 per decade. And there is no reason to believe that general problem solving software similar to that of humans will be lacking: there already exist mathematically optimal (though not yet practical) universal problem solvers developed at IDSIA. And existing highly practical (but not quite as universal) AI already learn from experience, outperforming humans in more and more fields. For example, biologically plausible deep / recurrent artificial neural networks are learning to solve pattern recognition tasks that seemed infeasible only 10 years ago. Examples: images, handwriting, traffic signs, since 2011 even with superhuman performance - no end in sight. Even creativity has been formalized such that it can now be implemented on machines. The current developments in AI may soon lead to the end of history as we know it (more), and as an IS master student you can become part of this revolution.

Artificial Intelligence systems have knowledge, beliefs, preferences and goals, and they have informational as well as motivational attitudes. They observe, learn, communicate, plan, anticipate and commit. They are able to reason about other systems and their own internal states, to simulate and optimize their performance. AI systems react to dynamic situations adapting their capabilities through learning mechanisms, with a high degree of autonomy.

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  • Study Programme

    In this master programme a wide variety of techniques will be taught, including intelligent robotics, artificial deep neural networks, machine learning, meta-heuristics optimization techniques, data mining, data analytics, simulation and distributed algorithms. The main courses are integrated with laboratory works where students have the possibility to use real robots and to practice with state of the art tools and methodologies. After the first few lectures of the basic Machine Learning course, AI master students will already know how to train self-learning artificial neural networks to recognize the images and handwritings to the right better than any other known method.


    First semester ECTS
    Core Courses  
    Machine Learning 6
    Deep Learning Lab 3
    Algorithms & Complexity 6
    Numerical Algorithms 3
    Elective courses 12


    Second semester ECTS
    Core Courses  
    Data Analytics 6
    Stochastic Methods 6
    Robotics 6
    Electives 12


    Third semester ECTS
    Core Courses  
    Artificial Intelligence 6
    Distributed Algorithms 6
    Master Thesis 9
    Electives 9


    Fourth semester ECTS
    Core Courses  
    Computer Vision & Pattern Recognition 6
    Geometric Deep Learning 3
    Master Thesis 21
    Electives 6


    Electives Fall semester ECTS
    Advanced Topics in Machine Learning 3
    Distributed Trust -Protocols and Techniques for Blockchains 3
    High-Performance Computing 6
    Introduction to Ordinary Differential Equations 3
    Introduction to Partial Differential Equations 6
    Mobile Computing 6
    Programming Styles 3
    Philosophy and Artificial Intelligence 3
    User Experience Design 6


    Electives Spring semester ECTS
    Advanced Computer Architectures 6
    Advanced Networking 6
    Business Intelligence and Applications 6
    Geometric Algorithms 6
    Multiscale Methods 6
    Quantum Computing 6
    Software Atelier: Simulation, Data Science & Supercomputing 6
  • Teaching

    Our Artificial Intelligence program boasts highly motivated teachers, who are dedicated and very active researchers both from the Faculty of Informatics and IDSIA.

    They are all actively involved in EU, FNS, CTI and industry projects. Their research activities cover subjects such as deep learning in neural networks, intelligent and collective robotics, data mining, nature-inspired optimization, simulation, approximation algorithms and intelligent information retrieval. The coursework material, software tools and robots reflects the latest state of the art. Further opportunities will arise through the thesis which may open the path towards PhD studies.

    Academic Director: Luca Maria Gambardella

    Co-Director: Jürgen Schmidhuber

  • Language

    Admission to English-language graduate-level (Master) programmes at USI require a good command of the English idiom. Non-English native speakers applying for such programmes, or whose previous degree was obtained in another language, are required to provide an internationally acknowledged language certificate equal to the B2 level, as defined by the Common European Framework of Reference for language learning (CEFR), or equivalent (e.g. TOEFL, IELTS, etc.).

    The B2 level on the CEFR corresponds to the following scores in internationally acknowledge exams:

    IELTS 5.5
    TOEFL Computer based: 183
    Internet based: 65
    Paper based: 513
    Cambridge English FCE (First Certificate English)
    TOEIC Listening & Reading: 785
    Speaking: 150
    Writing: 160

    Students admitted under the above mentioned condition (with the exception for the Master in Cognitive Psychology in Health Communication) must achieve a C1 competence in English within the maximum time required to obtain the Master's degree.
    The level can be certified either by attending a language course offered at USI during the Fall and Spring semester, and by taking the final exam, or by providing an internationally acknowledged language certificate*.

    *  The C1 level on the CEFR corresponds to the following scores in internationally acknowledge exams:

    IELTS 7.0
    TOEFL Internet based: 100
    Cambridge English CAE (Advanced certificate), grade C or above
    BEC (Business English), grade C or above
    TOEIC Listening & Reading: 945
    Speaking: 180
    Writing: 180


    As Lugano is located in the Italian-speaking part of Switzerland, students might be interested in acquiring the basics in Italian.
    The Università della Svizzera italiana offers a free of charge Italian language course: further information can be found here.