Foundations of Machine Learning
Slides
- Review of decision theory
Slides
Foundations of supervised learning
Supervised learning methods
- Ridge regression and Gaussian process priors
Slides
- Deep neural nets
Slides
- Conformal inference for prediction
Slides
Online learning and reinforcement learning
- Overview of online learning and reinforcement learning
Slides
- Online convex optimization
Slides
- Bandit problems
Slides
- Markov decision problems and reinforcement learning
Slides
Language models
- Large language models: Training and architecture
Slides
Ethics and machine learning
Bonus material
- Limiting experiments and the normal means model
Slides
- Reproducing Kernel Hilbert Spaces and Splines
Slides
- Applications of Gaussian process priors in economics
Slides
- Regression trees and random forests
Slides
- Double/debiased machine learning
Slides
- Variational auto-encoders and diffusion models for image generation
Slides
- Adversarial online learning
Slides
- Worst-case sequence for Thompson sampling
Slides
- Experiments for policy choice
Slides
Homework problems
- Approximation, estimation, and shrinkage: foundations_ml_ps1_2027.pdf
- Conformal prediction: foundations_ml_ps2_2027.pdf
- Multi-armed bandits: foundations_ml_ps3_2027.pdf
Class readers
Useful links
Machine learning books
Programming in Python
Programming in R
Data visualization