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Machine Learning

Descrizione

COURSE OBJECTIVES

Master's students will gain familiarity with state-of-the-art machine learning; focusing on neural networks and reinforcement learning

 

COURSE DESCRIPTION

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.

 

LEARNING METHODS

The course includes lectures, TA sessions, and graded exercise sheets including programming assignments.

 

EXAMINATION INFORMATION
Written exam and graded exercise sheets, including programming assignments.

 

REFERENCES
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.

Persone

 

Wand M.

Docente titolare del corso

Ashley D. R.

Assistente

Faccio F.

Assistente

Gopalakrishnan A.

Assistente

Herrmann V.

Assistente

Kirsch L. M.

Assistente

Informazioni aggiuntive

Semestre
Autunnale
Anno accademico
2021-2022
ECTS
6
Lingua
Inglese
Offerta formativa
Master of Science in Artificial Intelligence, Foundation course, 1° anno
Master of Science in Computational Science, Corso a scelta, 1° anno
Master of Science in Economics, Corso a scelta, Minor in Public Policy, 2° anno
Master of Science in Economics, Corso a scelta, Minor in Data Science, 2° anno
Master of Science in Financial Technology and Computing, Corso di base, 2° anno
Master of Science in Informatics, Foundation course, 1° anno
Master of Science in Informatics, Foundation course, 2° anno
Master of Science in Management and Informatics, Corso a scelta, 2° anno
Dottorato in Scienze informatiche, Corso a scelta, Corso, 1° anno (4 ECTS)
Dottorato in Scienze informatiche, Corso a scelta, Corso, 2° anno (4 ECTS)