Lecture

Linear Models for Classification

In course

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Description

This lecture covers linear models for classification, starting with the basics of parametric models like lines and hyperplanes, moving on to linear regression, binary classification, and logistic regression. It also delves into model evaluation metrics, the concept of margin between classes, and the formulation for a maximum margin classifier. The lecture introduces the notion of constrained optimization and discusses the challenges of overlapping classes in practical scenarios.

Instructor

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Official source

https://mediaspace.epfl.ch/media/0_x5g081ds

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Ontological neighbourhood

Statistics

Data analysis: Regression analysis, Statistical classification

Information engineering

Machine learning: Topics in machine learning, Artificial neural networks

Theoretical computer science

Algorithms and data structures: Combinatorial optimization

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