CS 189/289A at UC Berkeley

# Introduction to Machine Learning

Lectures: Tuesday, Thursday 2-3:30 pm in Li Ka Shing 245 *(Berkeley Academic Guide page)*

### Week 2 Overview

## Maximum Likelihood Estimation

- Discussion 0 (solution)
- Homework 0 (zip)
- Lecture 8/30 (slides)
- Lecture 9/1 (slides)

### Week 3 Overview

## Linear Regression

- Discussion 1 (solution)
- Homework 0 (zip)
- Homework 1 (zip)
- Lecture 9/6 (slides)
- Lecture 9/8 (slides)

### Week 5 Overview

## Gradient Descent and Backpropagation

### Week 9 Overview

## Decision Trees

### Week 10 Overview

## Convolutional Neural Networks and Vision

### Week 11 Overview

## Transformers and Natural Language Processing

### Week 12 Overview

## Graph Neural Networks and Computational Biology

### Week 13 Overview

## Hidden Markov Models and Graphical Models

### Week 14 Overview

## Reinforcement Learning

### Week 15 Overview

## Robotics and Causality

## Discussions

- Discussion 0: Math review and self-assessment (solution)
- Discussion 1: Maximum Likelihood Estimation, Regularization, LASSO (solution)
- Discussion 2: MLE vs. MAP, Regularization (solution)
- Discussion 3: Gradient Descent, Neural Networks (solution)
- Discussion 4: Backpropagation (solution)
- Discussion 5: Risk Minimization, Bias-Variance Tradeoff

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