• Cudré-Mauroux Philippe

• Cuccu Giuseppe

The goal of this course is to understand the foundation of Machine Learning as a field, providing the basis to master its many branches and applications. After an introduction about how machines learn, the focus will be on a short selection of key algorithms for supervised, unsupervised and reinforcement learning. The students will learn how parametrized function approximators can be used to take decisions, how to update their parametrization to modify their behavior, and how to leverage data and interactions in real-world applications.

The course is composed of theoretical lectures, explaining the inner working and intuitions behind the methods, interleaved with practical sessions to gain hands-on experience. The students will be introduced to both re-implementing (and customizing) some of the basic algorithms, and to applying the current standard libraries on practical applications.
The theoretical requirements include a basic understanding of linear algebra, calculus and statistic, as far as appropriate to understand the algorithms' inner workings. The practical sessions require a degree of familiarity with the Python programming language, and a working installation for the exercises. All lectures and material will be in English.

This course is set to provide a fundamental understanding of what is Machine Learning and how all of its methods operate, with practical experience on selected algorithms and libraries. The core objective is to support and facilitate further learning on the topic, both in the form of further self-study and as an introduction for advanced classes available in the Masters course.

Only for Master Students of the SES Faculty with finished Bachelor / Pre-Master Students SES Faculty not allowed!

Voir Règlement du 6 avril 2020 pour l’obtention des Bachelor of Science et des Master of Science, Art. 7.

Cours Master SES / 
Students of the SES faculty: registration only possible with acomplished Bachelor

Registration to the cours AND exams is mandatory and does not automatically happen if you are registered to a class. Please observe the deadlines of the faculty of science and medicine.

- Y. Abu-Mostafa, M. Magdon-Ismail, L. Hsuan-Tien. Learning from Data. AMLBook.
  http://amlbook.com/
- M. Bishop. Pattern Recognition and Machine Learning. Springer.
  https://www.springer.com/gp/book/9780387310732
- I. Goodfellow, Y. Bengio, A. Courville. Deep Learning. MIT Press.
  https://www.deeplearningbook.org/
- R. Sutton, A. Barto. Reinforcement Learning. MIT Press.
https://mitpress.mit.edu/books/reinforcement-learning-second-edition

Selon modalité A de l'annexe du plan d'études en informatique

Access requires passing 10 out of 13 of the weekly assignments

Open book exam

Selon modalité A de l'annexe du plan d'études en informatique

Access requires passing 10 out of 13 of the weekly assignments

Open book exam