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Probabilistic Graphical Model

討論平台

Topic	Week	Meeting Time
Introduction and Overview	(Week 1)
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