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Course

CS-526: Learning theory

Summary

Machine learning and data analysis are becoming increasingly central in many sciences and applications. This course concentrates on the theoretical underpinnings of machine learning.

Lectures

Instructors

Official source

https://edu.epfl.ch/coursebook/en/learning-theory-CS-526

Moodle Page

http://go.epfl.ch/CS-526

About this result

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Summary

Machine learning and data analysis are becoming increasingly central in many sciences and applications. This course concentrates on the theoretical underpinnings of machine learning.

Lectures

Instructors

Official source

https://edu.epfl.ch/coursebook/en/learning-theory-CS-526

Moodle Page

http://go.epfl.ch/CS-526

About this result

Lectures in this course (15)

PAC learning framework Log in to Mediaspace to watch this video

Covers the PAC learning framework, including domain, feature set, classifier, and empirical risk.

Uniform convergence and No-Free-Lunch theorem Log in to Mediaspace to watch this video

Explores uniform convergence, loss functions, and the No-Free-Lunch theorem in machine learning.

Tensors: Motivations and Introduction Log in to Mediaspace to watch this video

Covers the basics of tensors, including their definition, properties, and decomposition, starting with a motivating example involving Gaussian distributions.

Tensor Decomposition: Jennrich's Theorem Log in to Mediaspace to watch this video

Covers tensor decomposition and Jennrich's theorem, focusing on tensor rank and the uniqueness of tensor decomposition.

Matricizations and Alternating Least Squares Log in to Mediaspace to watch this video

Covers matricizations, ALS method, and tensor decomposition, emphasizing the alignment of fibers in matrices.

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