Lecture

Supervised Learning Essentials

Description

This lecture covers the fundamentals of supervised learning, including topics such as logistic regression, linear classification, and likelihood maximization. It delves into the concepts of probability distributions, noise modeling, and the importance of choosing the right loss function. The presentation also includes practical examples and exercises to reinforce the theoretical concepts.

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Official source

https://mediaspace.epfl.ch/media/0_sv0vj25t

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Ontological neighbourhood

Statistics

Data analysis: Regression analysis, Statistical classification

Statistical inference: Mathematical statistics, Statistical hypothesis testing

Information engineering

Machine learning: Topics in machine learning, Artificial neural networks

Signal processing: Digital image processing

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