CS 189 at UC Berkeley

Introduction to Machine Learning

Lectures: 2-3:30 pm Mon-Wed online (Berkeley Academic Guide page)

Anant Sahai

Make private Piazza post before emailing.

Office Hours: (see calendar)

Jennifer Listgarten

Make private Piazza post before emailing.

Office Hours: (see calendar)

Jitendra Malik

Make private Piazza post before emailing.

Office Hours: (see calendar)

Week 0 Overview

Welcome and Introduction

Week 1 Overview

Foundations: Regression, Classification, and Learning & Features and Regularization

Week 2 Overview

Foundations: Validation and Generalization

Week 3 Overview

Foundations: The probability perspective & Core tradeoffs

Week 4 Overview

Foundations: Kernel perspective & The nearest-neighbor perspective

Week 5 Overview

Foundations: Core tradeoffs revisited --- a unified view of under- and over-parameterized learning & Eigenspace perspectives

Week 6 Overview

Foundations: Dimensionality reduction and latent spaces & Feature selection and the role of sparsity

Week 7 Overview

Gradient descent and its role & Beyond linear models: neural networks

Week 8 Overview

Stochastic gradient descent & Natural losses for classification

Week 9 Overview

Generative approaches to classification & Clustering: basics


The discussion sections may cover new material and will give you additional practice solving problems. You should attend the discussion that you will be assigned to with your study group, and details about this will be made available on the course Piazza. See Syllabus for more information.


Lecture Slides

See Syllabus for more information.

  • Slides 8/26 (pdf)
  • Slides 8/31 (pdf)
  • Slides 9/2 (pdf)
  • Slides 9/9 (pdf)
  • Slides 9/14 (pdf)
  • Slides 9/16 (pdf)
  • Slides 9/18 (pdf)
  • Slides 9/21 (pdf)
  • Slides 9/23 (UPDATED) (pdf)
  • Slides 9/28 (Slides) (pdf)
  • Slides 9/28 (Notes) (pdf)
  • Slides 9/30 (Slides) (pdf)
  • Slides 9/30 (Notes) (pdf)
  • Slides 10/5 (Notes) (pdf)
  • Slides Backprop (10/12) (pdf)
  • Slides 10/12 (pdf)
  • Slides SGD Notes (10/14) (pdf)
  • Slides 10/14 (pdf)
  • Slides 10/19 (pdf)
  • Slides 10/21 (pdf)
  • Slides 10/26 (Slides) (pdf)
  • Slides 10/26 (Lecture Video) (pdf)