CS 189/289A at UC Berkeley

# Introduction to Machine Learning

Lectures: Tues & Thurs 2-3:30 pm online *(Berkeley Academic Guide page)*

### Week 1 Overview

## Maximum Likelihood and Multivariate Gaussians

- Discussion 0 (solution)
- Homework 0
- Lecture 8/31 (slides) (video)
- Lecture 9/2 (slides) (video)

### Week 2 Overview

## Linear Regression and Classification

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

### Week 3 Overview

## Logistic Regression and Neural Networks

- Discussion 2 (solution)
- Homework 1 (zip)
- Lecture 9/14 (slides) (slides) (video)
- Lecture 9/16 (slides) (video)

### Week 4 Overview

## Backpropagation, SGD, and Multiway Classification

### Week 5 Overview

## ROC Curves, Precision/Recall, Decision Theory, Bias-Variance, Over- and Under-Fitting

### Week 6 Overview

## PCA, Autoencoders, and Clustering

### Week 7 Overview

## t-SNE and Kernel Methods

- Midterm (Wed 10/13 7-9pm)