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/
Richard Sutton’s & Andrew Barto’s Reinforcement Learning: An Introduction (2nd Edition) book
EM algorithm
A gentle introduction: Expectation Maximization by Moritz Blume [pdf]
A Gentle Tutorial of the EM Algorithm and its Application to Parameter by J. A. Bilmes [pdf]
EM algorithm by Andrew Ng [pdf]
EM algorithm by Max Welling [pdf]
EM algorithm and mixtures by Brani Vidakovic [pdf]
EM algorithm and variants: an informal tutorial by Alexis Roche [pdf]
Notes on EM algorithm by Guillem Riambau-Armet [pdf]
Naive Bayes Model, Maximum-Likelihood Estimation, and EM Algorithm by Michael Collins [pdf]
PRML Chapter 9
GMM
Methods of Multivariate Analysis by Alvin [pdf]
IR