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CS 189 at UC Berkeley

Introduction to Machine Learning

Lectures: T/Th 12:30-2 p.m., 155 Dwinelle

Instructor Stella Yu

stellayu (at)

Office Hours: Tu/Th 2-3 p.m. 400 Cory (see calendar)

Professor Anant Sahai

sahai (at)

Office Hours: Tu/Th 2-3 p.m. 400 Cory (see calendar)

Week 1 Overview

Least Squares Framework

Week 2 Overview

Features, Regularization, Hyperparameters and Cross-Validation

Week 3 Overview

MLE, MAP, OLS, Bias-Variance Tradeoffs

Week 5 Overview

CCA, Feature Discovery

Week 6 Overview

Nonlinear LS, Gradient Descent

Week 7 Overview

Neural Nets, Stochastic Gradient Descent

Week 8 Overview

Regression for Classification: Generative v. Discriminative

Week 10 Overview

Kernel Methods, Nearest Neighbor Techniques

Week 11 Overview

Decision Trees, Boosting, Ensemble Methods

Week 12 Overview

Convolutional Neural Nets, Regularization Revisited

Week 13 Overview

Unsupervised Learning: Nearest Neighbors

Week 15 Overview

Clustering, Generative Adversarial Network


The discussion sections may cover new material and will give you additional practice solving problems. You can attend any discussion section you like. However, if there are fewer desks than students, then students who are officially enrolled in the course will get seating priority. See Syllabus for more information.