This is a short list of books and lectures with links for myself and anyone else who might find them useful. A book is worth a thousand arXiv papers!

### Machine Learning Books

- Pattern Recognition and Machine Learning by Christopher M. Bishop,
- Information Theory, Inference, and Learning Algorithms by David J.C. MacKay, videos,
- Machine Learning: A Probabilistic Perspective by Kevin P. Murphy,
- Bayesian Reasoning and Machine Learning by David Barber,
- Deep Learning by Ian Goodfellow, Yoshua Bengio, and Aaron Courville,
- The Elements of Statistical Learning by Trevor Hastie, Robert Tibshirani, and Jerome Friedman,
- All of Statistics A Concise Course in Statistical Inference by Larry Wasserman,
- Reinforcement learning: An introduction by Richard S. Sutton and Andrew G. Barto.

### Lectures on Machine Learning

- fast.ai,
- Natural Language Processing with Deep Learning, CS224n at Stanford,
- Convolutional Neural Networks for Visual Recognition CS231n at Stanford,
- Deep Unsupervised Learning, CS294-158 at UC Berkeley,
- Deep Reinforcement Learning CS285 at UC Berkeley.