課程網站 : Probabilistic Graphical Model

|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)

• Week1 (7/6~7/12)
  1. Group meeting
  2. Coursera Progress :