EC ENGR 246 · EE 246Foundations of Statistical Machine Learning

Introduction to foundations of statistical machine learning. Overview of several widely used learning algorithms including logistic and linear regression, kernel methods and support vector machine (SVM), ensemble learning methods, decisions trees and nearest neighbor classifiers. Connections to information theory through probably approximately correct (PAC) learning, stability, bias-complexity trade-off, structural risk minimization, minimum description length (MDL), and universal learning. Introduction to representation learning with topics including unsupervised learning, clustering, (non-linear) dimensionality reduction, sketching, parametric distribution estimation including Gaussian mixtures, expectation maximization, non-parametric distribution estimation, property testing and neural networks focused on distribution sampling (variational autoencoders ÝVAEs¨, generative adversarial networks ÝGANs¨). Discussion of reinforcement learning.

Letter grading.