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Course

EE-566: Adaptation and learning

Summary

In this course, students learn to design and master algorithms and core concepts related to inference and learning from data and the foundations of adaptation and learning theories with applications.

Lectures

Instructors

Official source

https://edu.epfl.ch/coursebook/en/adaptation-and-learning-EE-566

Moodle Page

http://go.epfl.ch/EE-566

About this result

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Summary

In this course, students learn to design and master algorithms and core concepts related to inference and learning from data and the foundations of adaptation and learning theories with applications.

Lectures

Instructors

Official source

https://edu.epfl.ch/coursebook/en/adaptation-and-learning-EE-566

Moodle Page

http://go.epfl.ch/EE-566

About this result

Lectures in this course (28)

Vector Differentiation and Convex Functions

Covers the concepts of vector differentiation and convex functions.

Subgradients and Convex Functions

Explores subgradients in convex functions, emphasizing non-differentiable yet convex scenarios and properties of subdifferentials.

Bayesian Inference: Gaussian Variables

Explores Bayesian inference for Gaussian random variables, covering joint distribution, marginal pdfs, and the Bayes classifier.

Bayesian Inference: Part 2

Explores Bayesian inference, multiclass classification, logistic regression, and linear regression inference.

Linear Regression: Mean-square-error Inference

Covers the MSE problem in linear regression models, focusing on the optimal estimator and data fusion methods.

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