Dates and Titles 
Topics 
Lecture Slides 
Suggested Further Readings 
Lecture 1
Introduction to PGM 

Gentle introduction

Probability distributions



Lecture 2
Conditional Independence
and Factorization 

Conditional independence

Factorization


 Chapter 2 in Jordan's PGM.

Lecture 3
Message Passing 

Elimination algorithm

Sum product algorithm

Max product algorithm

Bethe free energy

Factor graphs


 Chapter 3 and 4 in Jordan's PGM.
 J. S. Yedidia, W. T. Freeman, and Y. Weiss,
"Constructing freeenergy approximations and
generalized belief propagation algorithms,"
IEEE Trans. Information Theory, vol. 51, no. 7, 2009.

Lecture 4
Junction tree algorithm 


 Chapter 17 in Jordan's PGM.

Lecture 5
ChowLiu Tree 


 C. K. Chow and C. N. Liu,
"Approximating discrete probability distributions
with dependence trees,"
IEEE Trans. Information Theory, vol. 14, no. 3, 1968.

Lecture 6
Density Estimation 



Lecture 7
Variational Inference 

Variational Inference

Variational PCA

Variational MoG

Variational Bayesian Linear Regression

Variational Logistic Regression


 Murphy 21
 Bishop 10
 H. Attias (1999),
"Inferring parameters and structure of latent variable models
by variational Bayes,"
Proceedings of the 15th Conference on Uncertainty in Artificial
Intelligence, 1999.
 C. Bishop (1999),
"Bayesian PCA,"
NIPS1998.
 M. I. Jordan, Z. Ghahramani, T. S. Jaakkola, and L. K. Saul
"An introduction to variational methods for graphical models,"
In Learning in Graphical Models, Cambridge: MIT Press, 1999.
 T. S. Jaakkola and M. I. Jordan (2000),
"Bayesian logistic regression: a variational approach,"
Statistics and Computing, vol. 10, 2000.

Lecture 8
Topic models 

Probabilistic LSI

Latent Dirichlet allocation


 T. Hofmann (1999),
"Probabilistic latent semantic analysis,"
UAI, 1999.
 D. M. Blei, A. Ng, and M. I. Jordan (2003),
"Latent Dirichlet allocation,"
Journal of Machine Learning Research, 2003.
 D. M. Blei and M. I. Jordan (2003),
"Modeling annotated data,"
SIGIR, 2003.

Lecture 9
Bayesian recommendation 

Bayesian matrix factorization

Bayesian biomial mixture model for collaborative prediction



YongDeok Kim and Seungjin Choi (2014),
"Bayesian binomial mixture model for collaborative prediction with nonrandom missing data,"
RecSys2014, 2014

YongDeok Kim and Seungjin Choi (2014),
"Scalable variational Bayesian matrix factorization with side information,"
AISTATS2014, 2014.

Sunho Park, YongDeok Kim, and Seungjin Choi (2013),
"Hierarchical Bayesian matrix factorization with side information,"
IJCAI2013, 2013.

YongDeok Kim and Seungjin Choi (2013),
"Variational Bayesian view of weighted trace norm regularization for matrix factorization,"
IEEE Signal Processing Letters, 2013.

Lecture 10
Sampling methods 

Monte Carlo methods

Markov chain Monte Carlo

Sequential Monte Carlo


 Chapter 11 in Bishop's PRML.
 Chapter on sampling methods in Jordan's PGM.
 C. Andrieu, N. De Freitas, A. Doucet, and M. I. Jordan (2003),
"An introduction to MCMC for machine learning,"
Machine Learning,
vol. 50, pp. 543, 2003.

Lecture 11
Dirichlet processes 

Dirichlet processes

DP mixture models


 See the handout for the list of references

Lecture 12
Time series models 

Linear dynamical systems (LDS)

Hidden Markov models (HMM)


