Moving Beyond Linearity

Abstract: This talk introduces key techniques for modeling nonlinear relationships in supervised learning. We begin by examining polynomial regression and step functions, then develop more flexible approaches using basis functions and splines, including cubic splines and smoothing splines, to capture complex structure in data. The seminar also covers Generalized Additive Models (GAMs), which extend linear models by allowing nonlinear functions of predictors while retaining interpretability.

Computer scienceMathematics

Audience: general audience

Basic Notions and Applied Topology Seminar