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Study plan of the Master in Artificial Intelligence (MAI)

This page contains information for currently enrolled students.

For general information on the Master, please refer to:

www.usi.ch/mai

 

Study plan of the Master in Artificial Intelligence (MAI)

The study plan (also study programme or study curriculum) of the Master includes information on the structure of the programme.

 

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  • Study plan 2019-2021

    The study plan for students matriculated in academic year 2019/20 is available at the following page:

    www.usi.ch/en/education/master/artificial-intelligence/structure-and-contents

     

     

  • Study plan 2018-2020

    Study plan of the Master in Artificial Intelligence - curriculum 2018-2020

     

    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

    6

    Electives

    9
       

    Fourth semester

    ECTS

    Core Courses

     

    Computer Vision & Pattern Recognition

    6

    Geometric Deep Learning

    3

    Master Thesis

    21

    Electives

    6
       
       

    Electives Fall Semester

     

    Advanced Topics in Machine Learning

    3

    Blockchains - Protocols and Techniques for Distributed Trust

    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

    Geometric Algorithms

    6

    Multiscale Methods

    6

    Philosophy and Artificial Intelligence

    3

    Quantum Computing

    6

    Software Atelier: Simulation, Data Science & Supercomputing

    6

     

    Changes in the study plan may occurr. In case of discrepancies, or for any legal purpose, the study plan indicated by the Director of the Master or the Dean's office of the Faculty of Informatics shall prevail.

     

     

  • Study plan 2017-2019

    Study plan of the Master in Artificial Intelligence - curriculum 2017-2019

     

    In this master program 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 - Autumn semester 2017/18 ECTS
    Core Courses  
    Machine Learning 6
    Deep Learning Lab 3
    Algorithms & Complexity                                                                    6
    Numerical Algorithms 3
    Elective courses 12

     

    Second semester - Spring semester 2018 ECTS
    Core Courses  
    Computer Vision & Pattern Recognition                                            6
    Data Analytics 6
    Stochastic Methods 6
    Robotics 6
    Electives 6

     

    Third semester - Autumn semester 2018/19                                 ECTS
    Core Courses  
    Artificial Intelligence 6
    Distributed Algorithms 6
    Master Thesis 9
    Electives 9

     

    Fourth semester - Spring semester 2019                                     ECTS
    Core Courses  
    Geometric Deep Learning 3
    Master Thesis 21
    Electives 6

     

    Electives Fall semester ECTS
    Advanced Networking 6
    Cyber-security 3
    High-Performance Computing 6
    Introduction to Partial Differential Equations                                    6
    Mobile Computing 6
    Simulation & Data Sciences Seminar 3
    User Experience Design 6

     

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

     

    Changes in the study plan may occurr. In case of discrepancies, or for any legal purpose, the study plan indicated by the Director of the Master or the Dean's office of the Faculty of Informatics shall prevail.

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Updated on: 24/10/2019