# Reading List

Bayesian Inference and Gibbs Sampling

- Heinrich, Gregor. Parameter estimation for text analysis
- Resnik, Philip, and Eric Hardisty. Gibbs sampling for the uninitiated
- Knight, Kevin. Bayesian Inference with Tears-A tutorial workbook for natural language researchers
- Gershman, Samuel J., and David M. Blei. A tutorial on Bayesian nonparametric models

Deep Learning

- Deep Learning Reading List http://deeplearning.net/reading-list/
- UFLDL Tutorial http://ufldl.stanford.edu/wiki/index.php/UFLDL_Tutorial Version 2
- Neural Network Papers https://github.com/robertsdionne/neural-network-papers
- Deep Learning& Machine Learning Reading List from Dr. Xiangnan He in NUS.

Reinforcement Learning

- Denny’s blog http://www.wildml.com/2016/10/learning-reinforcement-learning/
- David Silver Reinforcement Learning Course in UCL
- Richard Suttons & Andrew Bartos Reinforcement Learning: An Introduction (2nd Edition) book

EM algorithm

- A gentle introduction: Expectation Maximization by Moritz Blume
- A Gentle Tutorial of the EM Algorithm and its Application to Parameter by J. A. Bilmes [pdf]
- EM algorithm by Andrew Ng
- EM algorithm by Max Welling
- EM algorithm and mixtures by Brani Vidakovic
- EM algorithm and variants: an informal tutorial by Alexis Roche
- Notes on EM algorithm by Guillem Riambau-Armet
- Naive Bayes Model, Maximum-Likelihood Estimation, and EM Algorithm by Michael Collins
- PRML Chapter 9

GMM

- Methods of Multivariate Analysis by Alvin