##DeepBayesianLearning - great course http://deepbayes.ru/#materials
##Roadmap:
https://www.metacademy.org/roadmaps/rgrosse/bayesian_machine_learning
##Course:
Bayesian Method in Machine Learning http://www.cse.wustl.edu/~garnett/cse515t/
Advanced Machine Learning http://www.seas.harvard.edu/courses/cs281/
Machine Learning and Probabilistic Model http://www.cedar.buffalo.edu/~srihari/CSE574/index.html
Probabilistic Graphical Models https://sailinglab.github.io/pgm-spring-2019/lectures/
##Book:
Pattern Recognition and Machine Learning (PRML) by Christopher M. Bishop
Bayesian Reasoning and Machine Learning (BRML) by David Barber. Freely available online.
Machine Learning A Probabilistic Perspective (MLAPP) by Keven.P.Murphy
##Generalized Linear Model http://data.princeton.edu/wws509/notes/
##Tools:
##Deep Learning: Bayesian Reasoning and Deep Learning http://blog.shakirm.com/wp-content/uploads/2015/10/Bayes_Deep.pdf
Discussion on Bayesian and Deep Learning https://www.quora.com/Why-are-very-few-schools-involved-in-deep-learning-research-Why-are-they-still-hooked-on-Bayesian-methods
Deep Learning Course
NLP: http://deeplearning.cs.cmu.edu/
Unsupervised Learning: http://ufldl.stanford.edu/tutorial/
CNN: http://vision.stanford.edu/teaching/cs231n/
Reinforcement Learning: http://www0.cs.ucl.ac.uk/staff/d.silver/web/Teaching.html
Deep Reinforcement Learning: http://rll.berkeley.edu/deeprlcourse/
Video: https://www.youtube.com/playlist?list=PL6Xpj9I5qXYEcOhn7TqghAJ6NAPrNmUBH
##More:
Advanced Topic Modeling http://mimno.infosci.cornell.edu/info6150/
Bayesian Analysis for NLP http://www.cs.columbia.edu/~scohen/bayesian/
Advanced NLP(Bayesian Methods) https://courses.engr.illinois.edu/cs598jhm/sp2013/index.html
##Inference Exact Inference - Exact to get the posterior distribution
* Belief propagation for trees
* Variable Elimination Algorithm
* Junction tree Algorithm
Approximate inference - Approximate the posterior distribution
* Variational Inference
* Mean field approximation
* Structured Variational approximation
* Expectation Propagation
* Variational Bayes for Bayesian Model
* Markov Random Field
* Variational message passing
* Loopy belief propagation
Sample Method - Approximate sample from the posterior distribution
* Markov Chain Monte Carlo - Gibss Sampling
* Rejection sampling
* Particle filtering
Maximum Likelihood
* Expectation Maximization
* Gradient Descent
Related with Gradient Descent
* stochastic gradient variational inference - Hoffman, M., Bach, F., and Blei, D. Online learning for latent dirichlet allocation
* stochastic gradient MCMC algorithms - Stochastic gradient riemannian langevin dynamics on the probability simplex