Linear Models: Basics

This lecture covers the fundamentals of linear models in machine learning, starting with a recap on data and insights. It then delves into simple parametric models like lines and planes, explaining how they are defined and used in linear regression. The lecture also explores multi-output linear regression, discussing how to predict multiple values using matrices. Additionally, it introduces the concept of decision boundaries and evaluates the performance of linear models through metrics like Mean Squared Error, Precision, Recall, and ROC curves. The instructor for this lecture is Mathieu Salzmann.

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