I’ll be live blogging my graduate course on machine learning this semester (Fall 2023). The course is based on the text *Patterns, Predictions, and Actions* by Moritz Hardt and myself.

### A Table of Contents

#### Part I: Prediction

Lecture 1: Introduction

Lecture 2: What is machine learning?

Lecture 3: The Perceptron

Lecture 4: Optimization

Lecture 5: Sequential Prediction

Lecture 6: Generalization

Lecture 7: Features

Lecture 8: Nonlinear Prediction Functions

Lecture 9: Neural Networks

Lecture 10: Generalization in Practice

Lecture 11: Datasets

Lecture 12: Internal and External Validity

Lecture 13: Elementary Policy Optimization

Lecture 14: Hypothesis Testing

Lecture 15: Algorithmic Fairness

Lecture 16: Randomized Controlled Experiments

Lecture 17: Causal Inference

Lecture 18: Causal Inference in the Wild

Lecture 19: Stochastic Optimization and Policy Optimization

Lecture 20: Regret minimization and the multi-armed bandit

Lecture 21: Decision making in dynamical systems

Lecture 22: Algorithms for decision making in dynamical systems

Lecture 23: Reinforcement learning

Lecture 24: Machine Learning and Computer Gameplay

Epilogue