Foundations of Machine Learning
Tentative Syllabus: Syllabus_ML_Oxford_2022.pdf
Foundations
- Review of decision theory Slides
- Probably approximately correct learning theory Slides
- Shrinkage in the normal means model Slides
Supervised learning
- Gaussian process priors and reproducing kernel Hilbert spaces Slides
- Regression trees and random forests Slides
- Deep neural nets Slides
- Double/debiased machine learning Slides
Online learning and active learning
- Overview of online learning and active learning Slides
- Online learning Slides
- Bandit problems Slides
- Reinforcement learning Slides
Ethics and machine learning
Class readers
Homework problems
Useful links
Machine learning books
- The Elements of Statistical Learning
- Understanding machine learning: From theory to algorithms
- Gaussian Processes for Machine Learning
- Deep Learning
- Reinforcement learning - An introduction
- The Ethical Algorithm
Programming in R
- An Introduction to R
- R for Data Science
- Advanced R
- Hands-On Machine Learning with R
- Bayesian statistics using Stan
RStan - Training neural nets using Keras and Tensorflow