Dr. Rina Dechter @ UCI

Dr. Rina Dechter - University of California at Irvine
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CompSci-276 Fall 2021, Reasoning in Graphical Models

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Course Reference

Instructor:

Days:

Time:

Room:

Zoom:

https://uci.zoom.us/j/98005125009

Piaza:

https://piazza.com/uci/fall2021/cs276

Office hours:

IMPORTANT NOTE: Some classes will be taught purely virtually via Zoom, while others will be taught in person. Please refer to the course calendar at the end of the page for updates on whether classes will be taught in person or virtually via Zoom.

Course Description

One of the main challenges in building intelligent systems is the ability to reason under uncertainty, and one of the most successful approaches for dealing with this challenge is based on the framework of Bayesian networks, also called graphical models. Intelligent systems based on Bayesian networks are being used in a variety of real-world applications including diagnosis, sensor fusion, on-line help systems, credit assessment, bioinformatics and data mining.

The objective of this class is to provide an in-depth exposition of knowledge representation and reasoning under uncertainty using the framework of Bayesian networks. Both theoretical underpinnings and practical considerations will be covered, with a special emphasis on dependency and independency models, on construction Bayesian graphical models and on exact and approximate probabilistic reasoning algorithms. Additional topics include: causal networks, learning Bayesian network parameters from data and dynamic Bayesian networks.

Prerequisites

Familiarity with basic concepts of probability theory.
Knowledge of basic computer science, algorithms and programming principles.
Previous exposure to AI is desirable but not essential.

Course material

The course will be based mostly on three sources:

Additional sources:

Russell and Norvig, Artificial Intelligence: A Modern Approach
Daphne Koller and Nir Friedman, Probabilistic Graphical Models: Principles and Techniques

A longer list including secondary references.

Some links to software and tools.

Course Topics

Introduction: probabilistic graphical models.
Bayesian and Markov networks: Representing independencies by graphs.
Building Bayesian networks.
Inference in Probabilistic models: Bucket-elimination (summation and optimization), Tree-decompositions, Join-tree/Junction-tree algorithm.
Graph properties: induced-width, tree-width, chordal graphs, hypertrees, join-trees.
Search in Graphical models: AND/OR search Spaces for likelihood, optimization queries.
Learning graphical models.
Approximate Bounded Inference: weighted Mini-bucket, belief-propagation, generalized belief propagation.
Approximation by Sampling: MCMC schemes, Gibbs sampling, Importance sampling.
Causal graphical models.

Assignments:

There will be periodic homework assignments and students will also be engaged in projects.

Grading Policy:

Homework (70%), class project (30%)

Course Project

(You will need to be logged into Google with you UCI account to access.)
Project Information Page
Project Sign-Up Sheet

Syllabus

Week	Topic	Lectures	Slides	Homework	Reading	Date

Week 1	Introduction and Background. Bayesian and Markov networks: Representing Independencies by graphs.	Lec 1 Lec 2	Slides 1 Slides 2		[Introduction] (a) Pearl ch. 1-2 (b) Darwiche ch. 1,3 (c) Russell-Norvig ch. 13 (d) Bayesian Networks [Bayesian Networks] (a) Pearl ch. 3 (b) Darwiche ch. 4	M 9/27 VIRTUAL W 9/29 VIRTUAL

Week 2	d-seperation in DAGs Building Bayesian networks.	Lec 3 Lec 4	Slides 3 Slides 4	HW 1	(a) Pearl ch. 3 (b) Darwiche ch. 4 (c) Darwiche ch. 5	M 10/4 VIRTUAL W 10/6 VIRTUAL

Week 3	Probabilistic Inference: Bucket-elimination (summation, optimization) Local structures CPTs and Induced width algorithms.	Lec 5 Rec Unavail. Lec 6	Slides 5			M 10/11 W 10/13

Week 4	Probabilistic Inference: Tree-decompositions: Join-tree/Junction-tree algorithm. Cluster tree elimination.	Lec 7 Lec 8	Slides 6	HW 2	Dechter Ch. 4, Darwiche Ch. 6 Dechter Ch. 5, 7.1, Darwiche Ch. 7-8	M 10/18 W 10/20

Week 5	Probabilistic Inference by search: AND/OR search spaces	Lec 9 Lec 10	Slides 7	HW 3	Dechter Ch. 6-7	M 10/25 W 10/27

Week 6	Learning graphical models.	Lec 11 Lec 12	Slides 8	HW 4	Darwiche Ch. 17	M 11/1 W 11/3

Week 7	Approximate algorithms by Bounded Inference.	Lec 13 Lec 14	Slides 9		Darwiche Ch. 14 Dechter Ch. 8-9	M 11/8 W 11/10

Week 8	Approximate Algorithms by Sampling: MCMC schemes.	Lec 15 Lec 16	Slides 10(a) Slides 10(b)		Darwiche Ch. 15 Paper: Cutset-Sampling	M 11/15 W 11/17

Week 9	Approximate Algorithms by Sampling: MCMC schemes (cont.)	Lec 17		HW 5		M 11/22 W 11/24 Cancelled

Week 10	Project presentations	Lec P1 Lec P2				M 11/29 VIRTUAL W 12/1 VIRTUAL

Finals Week	Project presentations	Lec P3				F 12/10 VIRTUAL