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

  1. Approximation, estimation, and shrinkage: foundations_ml_ps1_2027.pdf
  2. Conformal prediction: foundations_ml_ps2_2027.pdf
  3. Multi-armed bandits: foundations_ml_ps3_2027.pdf

Class readers

Machine learning books

Programming in Python

Programming in R

Data visualization