Probabilistic Graphical Model

討論方式

每週會開一頁Hackpad，讓大家可以共同編輯閱讀時想到的問題和解答。每週也會有一次討論會，由同學自願幫大家簡單介紹課程大綱和心得，同時帶大家檢討Hackpad未完成的問題。
時間：12:00~14:00
地點：R301

Topic	Week	Meeting Time
Introduction and Overview	(Week 1)	7-11
Bayesian Network Fundamentals	(Week 1)
Template Models	(Week 1)
ML-class Octave Tutorial	(Week 1, Optional)
Structured CPDs	(Week 2)
Markov Network Fundamentals	(Week 2)
Representation Wrapup: Knowledge Engineering	(Week 3)
Inference: Belief Propagation, Part 1	(Week 3)
Inference: Belief Propagation, Part 2	(Week 4)
Inference: MAP Estimation, Part 1	(Week 4)
Inference: MAP Estimation, Part 2	(Week 5)
Inference: Sampling Methods	(Week 5)
Inference: Temporal Models and Wrap-up	(Week 6)
Decision Theory	(Week 6)
ML-class Revision	(Week 6, Optional)
Learning: Overview	(Week 6)
Learning: Parameter Estimation in BNs	(Week 7)
Learning: Parameter Estimation in MNs	(Week 7)
Structure Learning	(Week 8)
Learning With Incomplete Data	(Week 9)
Learning: Wrapup	(Week 9)
Summary	(Week 9)

Name	Mail
張舒凱	b00902061@ntu.edu.tw
高瑞宗	fredericstgo@gmail.com