Lecture

Machine Learning Fundamentals

In course

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Description

This lecture covers the basics of machine learning, including supervised and unsupervised learning, regression, classification, dimensionality reduction, and clustering. It also delves into performance metrics such as mean-square error and error rate, as well as optimization techniques like convexity and gradient descent. The lecture further explores the concepts of overfitting, underfitting, and regularization, with practical examples and discussions on model evaluation and hyperparameter tuning.

Instructor

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Official source

https://mediaspace.epfl.ch/media/0_xhh7dktn

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Ontological neighbourhood

Information engineering

Machine learning: Artificial neural networks, Topics in machine learning, Unsupervised learning

Statistics

Data analysis: Regression analysis, Cross-validation

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