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CS-233(a): Introduction to machine learning (BA3)

Résumé

Machine learning and data analysis are becoming increasingly central in many sciences and applications. In this course, fundamental principles and methods of machine learning will be introduced, analyzed and practically implemented.

Séances de cours

Enseignant·e

Mathieu Salzmann

I am a Senior Researcher at EPFL-CVLab, and, since May 2020, an Artificial Intelligence Engineer at ClearSpace (50%). Previously, I was a Senior Researcher and Research Leader in NICTA's computer vision research group. Prior to this, from Sept. 2010 to Jan 2012, I was a Research Assistant Professor at TTI-Chicago, and, from Feb. 2009 to Aug. 2010, a postdoctoral fellow at ICSI and EECS at UC Berkeley under the supervision of Prof. Trevor Darrell. I obtained my PhD in Jan. 2009 from EPFL under the supervision of Prof. Pascal Fua.

Source officielle

https://edu.epfl.ch/coursebook/fr/introduction-to-machine-learning-ba3-CS-233-A

Page Moodle

http://go.epfl.ch/CS-233_a

À propos de ce résultat

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Résumé

Séances de cours

Enseignant·e

Mathieu Salzmann

Source officielle

https://edu.epfl.ch/coursebook/fr/introduction-to-machine-learning-ba3-CS-233-A

Page Moodle

http://go.epfl.ch/CS-233_a

À propos de ce résultat

Séances de ce cours (47)

Fondements de l'apprentissage automatique

Couvre les principes fondamentaux et les méthodes de l'apprentissage automatique, y compris les techniques d'apprentissage supervisé et non supervisé.

Régression linéaire : bases et applications

Couvre les bases de la régression linéaire, y compris la régression 1D, les dérivés, les gradients et les applications en apprentissage automatique.

Perceptron multicouche : entraînement et rétropropagation

Explore les défis de l'entraînement des Perceptrons multicouches et de l'algorithme de rétropropagation.

Deep Learning : réseaux neuronaux convolutifs

Présente les réseaux neuronaux convolutifs, en expliquant leur architecture, leur processus de formation et leurs applications dans les tâches de segmentation sémantique.

Comprendre les auto-encodeurs

Explore les autoencodeurs, des mappages linéaires en PCA aux mappages non linéaires, aux autoencodeurs profonds et à leurs applications.

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