Foundations of Machine Learning
Tentative Syllabus: Syllabus_ML_Oxford_2025.pdf
- Review of decision theory Slides
Foundations of supervised learning
- Supervised learning using Python Jupyter Notebook, Html version
- Probably approximately correct learning theory Slides
- Shrinkage in the normal means model Slides
Supervised learning methods
- Ridge regression and Gaussian process priors Slides
- Regression trees and random forests Slides
- Deep neural nets Slides
- Transformer models for natural language processing Slides
- Double/debiased machine learning Slides
Online learning and active learning
- Overview of online learning and active learning Slides
- Online learning Slides
- Online convex optimization Slides
- Bandit problems Slides
- Reinforcement learning 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
- Variational auto-encoders and diffusion models for image generation Slides
- Worst-case sequence for Thompson sampling Slides
- Experiments for policy choice Slides
Homework problems
- Supervised learning: foundations_ml_ps1.pdf
- Shrinkage estimation: foundations_ml_ps2.pdf
- Double/debiased estimation: foundations_ml_ps3.pdf
- Multiarmed bandits: foundations_ml_ps4.pdf
Class readers
Useful links
Machine learning books
- The Elements of Statistical Learning
- Understanding machine learning: From theory to algorithms
- Gaussian Processes for Machine Learning
- Introduction to Online Convex Optimization
- Deep Learning
- Speech and Language Processing
- Reinforcement learning - An introduction
- The Ethical Algorithm
Programming in Python
- A Whirlwind Tour of Python
- Python Data Science Handbook
- scikit-learn - Machine Learning in Python
- Vega-Altair: Declarative Visualization in Python