Lecture Slides (Statistical Methods in AI and ML, CS 6347)

• Introduction

• Propositional Logic and Probability Review; Read: KF Chapter 2, AD Chapters 2 and 3

• Representation: Bayesian Networks; Read: KF Chapter 3, AD Chapter 4

• Representation: Markov Networks; Read: KF Chapters 4 and 5

• Inference: Bucket Elimination; Read: RD Chapter 4, KF Chapter 9, AD Chapter 6

• Inference: Bucket Elimination, Deck 2; Read: RD Chapter 5, KF Chapter 9, AD Chapter 6

• Inference: AND/OR search spaces, Deck 2; Read: RD Chapter 6

• Logical Elimination and Conditioning;

• Approximate Inference: Sampling Algorithms; Read: AD Chapter 15, KF Chapter 12

• Iterative Join Graph Propagation and Inference as Optimization, Read: AD Chapter 14, KF Chapter 11

• MPE and MAP inference, Read: AD Chapter 10, KF Chapter 13.

• Local search for MPE and MAP inference and Machine Learning Fundamentals Read: AD Chapter 10, KF Chapter 13.

• Parameter Learning in Bayesian networks Read: AD Chapter 17, KF Chapter 17.

• Parameter Learning in Bayesian networks using the Bayesian Approach April Read: AD Chapter 17,18, KF Chapter 17,19.

• Structure Learning in Bayesian Networks Read: AD Chapter 17,18, KF Chapter 17,18,19.

• Learning Markov Networks, Read: KF Chapter 20.

• Dynamic probabilistic models

• Recap

• Markov Logic

Notes: KF: Koller&Friedman's book; AD: Adnan Darwiche's book; RD: Rina Dechter's book