##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:

http://mc-stan.org/

##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