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Structure and contents

Prof. Jürgen Schmidhuber
Prof. Jürgen Schmidhuber, Director of the Master in Artificial Intelligence

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 2020-2022

    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

     

    Algorithms & Complexity

    6

    Deep Learning Lab

    3

    Machine Learning

    6

    Numerical Algorithms

    3

    Elective courses

    12
       

    Second Semester

    ECTS

    Core Courses

     

    Data Analytics

    6

    Robotics

    6

    Stochastic Methods

    6

    Electives

    12
       

    Third Semester

    ECTS

    Core Courses

     

    Artificial Intelligence

    6

    Distributed Algorithms I

    3

    Distributed Algorithms II  - Protocols and Techniques of Blockchains

    3

    Master Thesis

    9

    Electives

    9
       

    Fourth semester

    ECTS

    Core Courses

     

    Computer Vision & Pattern Recognition

    6

    Graph Deep Learning

    3

    Master Thesis

    21

    Electives

    6
       
       

    Electives Autumn Semester

     

    Advanced Topics in Machine Learning

    3

    High-Performance Computing

    6

    Introduction to Ordinary Differential Equations

    3

    Introduction to Partial Differential Equations

    6

    Mobile and Wearable Computing

    6

    Programming Styles

    3

    User Experience Design

    6
       

    Electives Spring Semester

     

    Advanced Computer Architectures

    6

    Advanced Networking

    6

    Business Intelligence and Applications

    6

    Effective High-Performance Computing & Data Analytics Summer School

    6

    Geometric Algorithms

    6

    Philosophy and Artificial Intelligence

    3

    Quantum Computing

    6

    Software Atelier: Simulation, Data Science & Supercomputing

    6

    Solution and Optimization Methods for Large Scale Problems

    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

    The admission to English-language Master programmes at USI requires a good command of English. Non-native English speakers applying to the Master’s 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 acknowledged exams:

    IELTS*

    5.5

    TOEFL*

    Computer-based: 183
    Internet-based*: 65
    Paper-based: 513

    Cambridge English

    B2 First

    TOEIC

    Listening & Reading: 785
    Speaking: 150
    Writing: 160

    *Due to the current health emergency, USI temporarily accepts TOEFL iBT Special Home Edition and IETLS Indicator as alternatives to the standard tests.
    TOEFL: https://www.ets.org/s/cv/toefl/at-home/
    IELTS: https://www.ieltsindicator.com/

     

    Students admitted under the above-mentioned condition 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 acknowledged exams:​

    IELTS

    7.0

    TOEFL

    Internet-based: 100

    Cambridge English

    C1 Advanced, grade C or above
    BEC (Business English), grade C or above

    TOEIC

    Listening & Reading: 945
    Speaking: 180
    Writing: 180

    We only accept the English qualifications mentioned above. The certificate must still be valid at the point of the application. In general, we are not able to accept language test results older than 3 years, as of September 1st.  

     

    Italian

    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.

    Exceptional Measure for the A.Y. 2020/21: when do I have to submit my English language certification?

    • Faculty of Communication, Culture and Society / Economics / Informatics: by 30 December 2020. 

      If a student/student fails to obtain the required level of English by the deadline, enrolment in the 2021 winter session will be cancelled, the exams taken in the intermediate session will be invalidated and admission to the Master's degree will not be confirmed.
      ​Candidates will also be required to reach level C1 by the deadline for obtaining the degree.