Course: STAT 37779
Title: Kernel Methods for Statistics and Machine Learning
Instructor: Anirban Chatterjee
Class Schedule: Sec 1: TR 3:30 PM–4:50 PM in TBA
Description: This course provides a rigorous introduction to kernel methods across statistics and machine learning, covering foundational concepts in kernels and reproducing kernel Hilbert spaces (RKHS), classical statistical tools such as kernel density estimation and kernel regression, and modern kernel-based inference techniques including discrepancy measures and hypothesis testing with MMD and related methods. The course then connects these ideas to contemporary machine learning by exploring the Neural Tangent Kernel (NTK) and kernel-based objectives in generative modeling, such as MMD GANs.