Introduction to Classification in Machine Learning

Abstract: This talk, based on Chapter 4 of An Introduction to Statistical Learning with Applications in Python, explores key methods for classification, including logistic regression, discriminant analysis, and $k$-nearest neighbors. We’ll discuss how these approaches model categorical outcomes, evaluate their performance using metrics like accuracy and receiver-operating characteristic curve curves, and demonstrate their implementation through practical Python examples.

Computer scienceMathematics

Audience: general audience

Basic Notions and Applied Topology Seminar