Technical

Some useful free technical references.

Linear Algebra

• Matrix Cookbook (Petersen & Pedersen)

• Matrix Algebra (Minka)

Optimization

• Convex Optimisation (Boyd & Vandenberge)

Supervised & Unsupervised Learning

• The Elements of Statistical Learning (Hastie, Tibshirani & Friedman)

• Introduction to Statistical Learning (James, Witten, Hastie, Tibshirani)

• Bayesian Reasoning and Machine Learning (Barber)

• Information Theory, Inference and Learning Algorithms (MacKay)

• Graphical Models (Jordan)

• Multi-Task Learning tutorial (Zhou, Chen, Ye)

• Gaussian Processes (Rasmussen, Williams)

• Algorithms for Reinforcement Learning (Szepesvári)

Time Series, Econometrics

• Notes on Time Series (Weber)

• Econometrics (Hansen)

Machine learning vs. Statistics

• Glossary (Tibshirani)