CS 189 at UC Berkeley
Introduction to Machine Learning
Lectures: 2-3:30 pm Mon-Wed online (Berkeley Academic Guide page)
Week 0 Overview
Welcome and Introduction
- Discussion 0 (solution)
- Homework 0 (zip, datahub)
- Slides 8/26 (download)
Week 1 Overview
Foundations: Regression, Classification, and Learning & Features and Regularization
- Note 2 : Linear Regression
- Note 3 : Feature Engineering
- Discussion 1
- Homework 0 (zip, datahub)
- Homework 1 (zip, datahub)
- Slides 8/31 (download)
- Slides 9/2 (download)
Week 2 Overview
Foundations: Validation and Generalization
- Note 3 : Feature Engineering
- Discussion 2 (solution)
- Homework 1 (zip, datahub)
- Homework 2 (zip, datahub)
- Slides 9/9 (download)
Week 3 Overview
Foundations: The probability perspective & Core tradeoffs
- Note 4 : MLE and MAP for Regression
- Note 6 : Multivariate Gaussians
- Discussion 3 (solution)
- Homework 2 (zip, datahub)
- Homework 3 (zip, datahub)
- Slides 9/14 (download)
- Slides 9/16 (download)
- Slides 9/18 (download)
Week 4 Overview
Foundations: Kernel perspective & The nearest-neighbor perspective
- Note 5 : Bias-Variance Tradeoff
- Note 7 : MLE and MAP Pt. 2
- Note 8 : Kernels and Ridge Regression
- Discussion 4
- Homework 3 (zip, datahub)
- Homework 4 (zip, datahub)
- Slides 9/21 (download)
- Slides 9/23 (UPDATED) (download)
Week 5 Overview
Foundations: Core tradeoffs revisited --- a unified view of under- and over-parameterized learning & Eigenspace perspectives
- Note 8 : Kernels and Ridge Regression
- Homework 4 (zip, datahub)
- Slides 9/23 (UPDATED) (download)
- Slides 9/28 (Slides) (download)
- Slides 9/28 (Notes) (download)
Discussions
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.
- Discussion 0: Discussion logistics in the zoom era (solution)
- Discussion 1: Learning 1d function
- Discussion 2: Test sets and validation (solution)
- Discussion 3: Probabilistic View of Linear Regression (solution)
- Discussion 4: Kernels and kernelization
Expand
Homeworks
Your two lowest homework scores will be dropped and homework scores are rescaled such that 60% is full credit, but these drop should be reserved for emergencies. See Syllabus for more information.
- Homework 0: Getting Started (zip, datahub)
- Homework 1: Classification and Regression (zip, datahub)
- Homework 2: Validation and setting hyperparameters (zip, datahub)
- Homework 3: Probability-based Models (zip, datahub)
- Homework 4: Kernel regression (zip, datahub)
Expand