Lecture

Linear Models for Classification

Description

This lecture covers linear models for classification, starting with simple parametric models and progressing to hyperplanes in higher dimensions. It introduces logistic regression, loss functions, and empirical risk minimization. The instructor explains multi-output linear regression, gradient computation, and the logistic sigmoid function. The lecture concludes with a discussion on minimizing functions using gradient descent and the challenges of non-convex optimization.

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.