<p>Lecture, four hours; discussion, one hour; outside study, seven hours. Enforced requisites: course 131A, Mathematics 33A. 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 [VAEs], generative adversarial networks [GANs]). Discussion of reinforcement learning. Letter grading.</p>

Electrical and Computer Engineering

Known as the birthplace of the Internet and where countless other cutting-edge technologies took their first steps, the school is consistently ranked among the top 10 public engineering schools in the U.S.

Henry Samueli School of Engineering and Applied Science

Electrical and computer engineering continues to make vital contributions to expand access, processing, and control of the vast amount of information and data in the world.

General Catalog

Foundations of Statistical Machine Learning

ECENGR246

4 units