Master's students will gain familiarity with state-of-the-art machine learning; focusing on neural networks and reinforcement learning
Introductory Master's Course to Artificial Intelligence (AI), taught by experts of the award-winning Swiss AI Lab IDSIA. Machine Learning (ML) is both a cornerstone of AI and a top skill sought by IT employers. Today ML is everywhere: search engines use it to improve answers, email programs use it to filter spam, banks use it to predict stock markets, doctors use it to recognize tumors, robots use it to localize themselves and to understand their environment, video games use it to enhance the player's experience, smartphones use it to recognize objects / faces / gestures / voices / music, etc. This course covers both basic theory and challenging applications in the field, after a few lectures, students will already be able to train a neural network to recognize images better than with any other known method.
Required prior knowledge
Equivalent of bachelor level courses in (1) linear algebra, (2) analysis, (3) probability theory and statistics, (4) programming.
The course includes lectures, TA sessions, and graded exercise sheets including programming assignments.
Written exam and graded exercise sheets, including programming assignments.
There is no compulsory textbook.
For further reading on the basics of machine learning we suggest:
- Mathematics for Machine Learning by Marc Peter Deisenroth, A. Aldo Faisal, and Cheng Soon Ong: Cambridge University Press, 2020.
Available online: https://mml-book.github.io/
- Pattern Recognition and Machine Learning by Christopher M. Bishop: Springer, 2006.
- References for state-of-the-art methods will be given in the course.
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Master of Science in Economics, Elective course, Minor in Data Science, 2nd year
Master of Science in Financial Technology and Computing, Core course, 2nd year
Master of Science in Informatics, Foundation course, 1st year
Master of Science in Informatics, Foundation course, 2nd year
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PhD programme of the Faculty of Informatics, Elective course, Lecture, 2nd year (4 ECTS)