Probabilistic Graphical Model (Fall 2012, 3 credits)
Instructor: Prof. Shoude Lin (sdlin@csie.ntu.edu.tw)
Classroom: CSIE 101
Meeting Time: Tu 2:205:20 pm
Office Hour: Tu after class or by appointment
TA : Chungyi Li (r00922051@csie.ntu.edu.tw), Tingwei Lin (b97083@csie.ntu.edu.tw), Enhsu Yen (a061105@gmail.com)
Course Description:
Realworld events are full of uncertainty. Probabilistic Theory is a way to model uncertainty. However, probabilistic theory itself can hardly be exploited to deal with largescale realworld problem which contains many correlated variables. Thanks to the development of a new reasoning and inference framework called probabilistic graphical models, we are then able to deal with thousands or even more variables altogether in a efficient manner. This course will cover the basic and advanced topics about probabilistic graphical models, including directed models such as Bayesian Networks, undirected models such as Markov network, and the corresponding inference and learning methods such as variable elimination, brief propagation, EM alrogihtm, and Markov chain Monte Carlo methods.
Grading:
Three Homework assignments (70%), three persons per team
Final Project (30%)
Textbook:
Bayesian Reasoning and Machine Learning, David Barber, Cambridge 2012 (pdf version available online)
Probabilistic Graphical Models Principles and Techniques Daphne Koller and Nir Friedman (ISBN 0262013193)
Recommend Readings:
 Elements of Statistical Learning by Trevor Hastie,
Robert Tibshirani and Jerome Friedman (ISBN 0387952845)
 Pattern Recognition and Machine Learning by Chris
Bishop (SBN 0387310738)
 Machine Learning by Tom Mitchell (ISBN 0070428077)
 The EM algorithm and related statistical models /
edited by Michiko Watanabe, Kazunori Yamaguchi
Syllabus:
Research Methods  First section (2:203:20)  Chpaters  HW  
11Sep  Introduction  CH1~2  
18Sep  representation  Bayesian Networks, PGM tool  CH3  HW11 
25Sep  representation  BN, Case Study  CH3  
2Oct  Inference  Markov Networks  CH4  HW12 
9Oct  Inference  Exact Inference  CH5  
16Oct  Inference  Exact Inference  CH6  HW1 due HW21 out 
23Oct  Inference  Sampling as approximate inference  CH27  HW22 out 
30Oct  Learning  Deterministic Approximate Inference  CH28  
6Nov  Learning  HW1 discussion, statistics for ML  CH8.6~8.8,T  HW2 due 
13Nov  Learning  Learning as Inference (learning for BN), MLE, MAP, 
CH9.1~9.4 T, 

20Nov  Learning  MLE for undirected models, Naive Bayes  CH9.6 CH10, C  HW3 out 
27Nov  Learning  Learning with hidden variables (EM1)  CH11  
4Dec  Learning  Learning with hidden variables (EM2)  CH11  HW3 due 
11Dec  Learning  Project Proposal  
18Dec  Case study  Structure learning & Bayesian Model Selection  CH9.5, 12.1~12.5 C  
25Dec  Case study  Hidden Markov Model  CH23  
8Jan  Final project  Final project presentation 