CS 189 at UC Berkeley

Introduction to Machine Learning

Lectures: T/Th 3:30-5 p.m., 155 Dwinelle

Professor Jennifer Listgarten

jennl [at] berkeley.edu

Office Hours: Tu/Th 5-6 p.m. (see calendar)

Professor Anant Sahai

sahai (at) eecs.berkeley.edu

Office Hours: Tu/Th 5-6 p.m. (see calendar)

Week 0 Overview

Least Squares Framework

Week 1 Overview

Features, Regularization, Hyperparameters and Cross-Validation

Week 2 Overview

MLE, MAP, OLS, Bias-Variance Tradeoffs

Week 3 Overview

Weighted LS, Total LS, Eigenmethods

Week 5 Overview

CCA, Nonlinear LS, Gradient Descent

Week 6 Overview

Neural Nets, Stochastic Gradient Descent

Week 7 Overview

Regression for Classification: Generative v. Discriminative

Week 8 Overview

Loss Functions, Hinge-Loss, SVM

Week 9 Overview

k-Means, EM


See Syllabus for more information. You can find a list of week-by-week topics. You can find a comprehensive compilation of the notes here.



The discussion sections may cover new material and will give you additional practice solving problems. You can attend any discussion section you like. See Syllabus for more information.



All homeworks are partially graded and it is highly-recommended that you do them. Your lowest homework score will be dropped, but this drop should be reserved for emergencies. Here is the semester's self-grade form (See form for instructions). See Syllabus for more information.