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Foundations of Machine Learning

ECTS : 4

Volume horaire : 24

Description du contenu de l'enseignement :

The course will introduce the theoretical foundations of machine learning, review the most successful algorithms with their theoretical guarantees, and discuss their application in real-world problems. The covered topics are:

Compétence à acquérir :

The aim of this course is to provide the students with the fundamental concepts and tools for developing and analyzing machine learning algorithms.

Mode de contrôle des connaissances :

- Each student will have to have the role of scribe during one lecture, taking notes during the class and sending the notes to the teacher in pdf.
- Final exam

Bibliographie, lectures recommandées :

The most important book: - Shalev-Shwartz, S., & Ben-David, S. (2014). Understanding machine learning: From theory to algorithms. Cambridge University Press. Also: - Mohri, M., Rostamizadeh, A., & Talwalkar, A. (2012). Foundations of machine learning. MIT press. - Vapnik, V. (2013). The nature of statistical learning theory. Springer science & business media. - Bishop Ch. (2006). Pattern recognition and machine learning. Springer  - Friedman, J., Hastie, T., & Tibshirani, R. (2001). The elements of statistical learning (Vol. 1, No. 10). New York, NY, USA: Springer series in statistics. - James, G., Witten, D., Hastie, T., & Tibshirani, R. (2013). An introduction to statistical learning (Vol. 112). New York: Springer.  

Document susceptible de mise à jour - 01/04/2026
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