Foundations of Machine Learning - Oxford summerschool 2022

Tentative Syllabus: Syllabus_ML_Oxford_summerschool_2022.pdf

Summer school homepage: september-summer-school

Lecture slides

1. Probably approximately correct learning theory Slides
2. Shrinkage in the normal means model Slides
3. Deep neural nets Slides
   Double/debiased machine learning Slides
4. Overview of online learning and active learning Slides
   Online learning Slides
   Bandit problems Slides
5. Fairness Slides
   Differential privacy Slides

Problems for the practice sessions

1. Practice session Wednesday: foundations_ml_ps3.pdf
2. Practice session Friday: foundations_ml_ps5.pdf

Class readers

• Foundations.zip
• Supervised.zip
• Active.zip
• Ethics.zip

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

Data visualization

• Data Visualization - A Practical Introduction
• An Economist’s Guide to Visualizing Data
• ggplot2 - Elegant Graphics for Data Analysis