CS 189 (CDSS) at UC Berkeley

# Introduction to Machine Learning

Lectures: Monday, Wednesday, & Friday 1-2 pm in Evans 10 *(Berkeley Academic Guide page)*

### Week 1 Overview

## Welcome and Introduction

- Homework 0
- Lecture 1/19 (slides) (video)
- Lecture 1/21 (slides) (video)

### Week 2 Overview

## Maximum Likelihood Estimation

- Discussion 0 (solution)
- Homework 0
- Lecture 1/24 (slides) (video)
- Lecture 1/26 (slides) (video)
- Lecture 1/28 (slides) (video)

### Week 3 Overview

## Linear Regression and MAP Estimation

- Discussion 1 (solution)
- Homework 1
- Lecture 1/31 (slides) (video)
- Lecture 2/2 (slides) (video)
- Lecture 2/4 (slides) (video)

### Week 4 Overview

## Logistic Regression and Optimization

- Discussion 2 (solution)
- Homework 1
- Lecture 2/7 (slides) (video)
- Lecture 2/9 (slides) (video)
- Lecture 2/11 (slides) (video)

### Week 5 Overview

## Support Vector Machines

- Discussion 3 (solution)
- Homework 2 (zip)
- Lecture 2/14 (slides) (video)
- Lecture 2/16 (slides) (video)
- Lecture 2/18 (slides) (video)

### Week 6 Overview

## Kernels

- Discussion 4 (solution)
- Homework 2 (zip)
- Lecture 2/23 (slides) (video)
- Lecture 2/25 (slides) (video)

### Week 7 Overview

## Analytical Techniques in ML

- Discussion 5 (solution)
- Homework 3 (zip)
- Lecture 2/28 (slides) (video)
- Lecture 3/2 (slides) (video)
- Lecture 3/4 (slides) (video)

### Week 9 Overview

## Nearest Neighbors and Decision Trees

- Discussion 7 (solution)
- Homework 3 (zip)
- Lecture 3/14 (slides) (video)
- Lecture 3/16 (slides) (video)
- Lecture 3/18 (slides) (video)

### Week 10 Overview

## Dimensionality Reduction and Clustering

- Discussion 8 (solution)
- Homework 4 (zip)
- Lecture 3/28 (slides) (video)
- Lecture 3/30 (slides) (video)
- Lecture 4/1 (slides) (video)

### Week 11 Overview

## Neural Networks

- Discussion 9 (solution)
- Homework 4 (zip)
- Homework 5 (link)
- Lecture 4/4 (slides) (video)
- Lecture 4/6 (slides) (video)
- Lecture 4/8 (slides) (video)

### Week 12 Overview

## Convolutional Neural Networks

- Discussion 10 (solution)
- Homework 5 (link)
- Lecture 4/11 (slides) (video)
- Lecture 4/13 (slides) (video)
- Lecture 4/15 (slides) (video)

### Week 13 Overview

## Transformers

- Discussion 11 (solution)
- Homework 6
- Lecture 4/18 (slides) (video)
- Lecture 4/20 (slides) (video)
- Lecture 4/22 (slides) (video)

### Week 14 Overview

## Special Topics

- Discussion 12 (solution)
- Homework 6
- Lecture 4/25 (slides) (video)
- Lecture 4/27 (slides) (video)
- Lecture 4/29 (slides) (video)

## Discussions

- Discussion 0: Math review and self-assessment (solution)
- Discussion 1: Maximum Likelihood Estimation (solution)
- Discussion 2: MAP Estimation and LASSO Regression (solution)
- Discussion 3: Logistic Regression and Gaussian Discriminative Analysis (solution)
- Discussion 4: Support Vector Machines (solution)
- Discussion 5: Kernels (solution)
- Discussion 6: Midterm Review (solution)
- Discussion 7: Risk Minimization and Bias Variance (solution)
- Discussion 8: Decision Trees and K-Nearest Neighbors (solution)
- Discussion 9: Principal Component Analysis (solution)
- Discussion 10: Neural Networks (solution)
- Discussion 11: Convolutional Neural Networks (solution)
- Discussion 12: Attention and Transformers (solution)

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## Homeworks

- Homework 0: Math review and self-assessment
- Homework 1: Multivariate Gaussians and Regression
- Homework 2: Logistic Regression, GDA, and SVMs (zip)
- Homework 3: Kernels (zip)
- Homework 4: Decision Trees and K-Nearest Neighbors (zip)
- Homework 5: PCA and Neural Networks (link)
- Homework 6: CNNs and Transformers

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## Lecture Slides and Videos

- 1/19: Welcome and Introduction

(slides) (video) - 1/21: Math Review

(slides) (video) - 1/24: MLE 1

(slides) (video) - 1/26: MLE 2

(slides) (video) - 1/28: Linear Regression 1

(slides) (video) - 1/31: Multivariate Gaussians

(slides) (video) - 2/2: MAP Estimation

(slides) (video) - 2/4: Linear Regression 2

(slides) (video) - 2/7: Classification for Gaussians

(slides) (video) - 2/9: Logistic Regression

(slides) (video) - 2/11: Optimization

(slides) (video) - 2/14: SVMs 1

(slides) (video) - 2/16: SVMs 2

(slides) (video) - 2/18: SVMs 3

(slides) (video) - 2/23: Kernels 1

(slides) (video) - 2/25: Kernels 2

(slides) (video) - 2/28: Validation Sets

(slides) (video) - 3/2: Bias-Variance and Decision Theory

(slides) (video) - 3/4: Precision, Recall, and ROC Curves

(slides) (video) - 3/14: Nearest Neighbors

(slides) (video) - 3/16: Decision Trees 1

(slides) (video) - 3/18: Decision Trees 2

(slides) (video) - 3/28: PCA 1

(slides) (video) - 3/30: PCA 2

(slides) (video) - 4/1: Clustering

(slides) (video) - 4/4: Neural Networks 1

(slides) (video) - 4/6: Neural Networks 2

(slides) (video) - 4/8: Neural Networks 3

(slides) (video) - 4/11: Convolutional Neural Networks 1

(slides) (video) - 4/13: Convolutional Neural Networks 2

(slides) (video) - 4/15: OPTIONAL: Unsupervised Learning Methods

(slides) (video) - 4/18: Transformers 1

(slides) (video) - 4/20: Transformers 2

(slides) (video) - 4/22: Transformers 3

(slides) (video) - 4/25: Guest Speaker: Recommender Systems

(slides) (video) - 4/27: Guest Speaker: Fairness

(slides) (video) - 4/29: OPTIONAL: Finale

(slides) (video)

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