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