Lecture

Linear Models: Part 1

Description

This lecture introduces linear models, starting with a recap on data attributes and insight into supervised and unsupervised learning algorithms. It covers simple parametric models like lines and planes, linear regression, derivatives, gradients, and hyperplanes. The lecture delves into minimizing risk, empirical risk minimization, and the transition from regression to classification. It also discusses multi-output linear regression, decision boundaries, and the evaluation metrics for regression and classification problems.

About this result
This page is automatically generated and may contain information that is not correct, complete, up-to-date, or relevant to your search query. The same applies to every other page on this website. Please make sure to verify the information with EPFL's official sources.

Graph Chatbot

Chat with Graph Search

Ask any question about EPFL courses, lectures, exercises, research, news, etc. or try the example questions below.

DISCLAIMER: The Graph Chatbot is not programmed to provide explicit or categorical answers to your questions. Rather, it transforms your questions into API requests that are distributed across the various IT services officially administered by EPFL. Its purpose is solely to collect and recommend relevant references to content that you can explore to help you answer your questions.