Explore logistic regression and generalized linear models in this comprehensive lecture. Delve into key topics including the exponential family, probable discredited models, and Newton's method. Learn how to apply logistic regression for classification tasks and understand its role in app recommendation systems. Examine case studies and discover the importance of sparsity in model development. Gain valuable insights into these fundamental machine learning concepts and their practical applications.
Logistic Regression and Generalized Linear Models
Overview
Syllabus
Introduction
Recap
The exponential family
Probable discredited models
Logistic regression
Logistic regression and classification
Newtons method
Case study
App recommendation
Classification
Sparsity
Taught by
Pascal Poupart
