CS 262A - Learning and Reasoning with Bayesian NetworksInstructor: Professor Guy Van den Broeck <firstname.lastname@example.org>
TA: Umut Oztok <email@example.com>
Time: Winter 2016, MW 12pm-1:50pm
Place: BOELTER 2760
Instructor Office Hours: M 3:30pm-4:30pm at BOELTER 4531E
TA Office Hours: W 2:30pm-3:30pm (or by appointment) at BOELTER 4663
One of the main challenges in building intelligent systems is the ability to learn and reason under uncertainty, and one of the most successful approaches for dealing with this challenge is based on the framework of Bayesian belief networks (and probabilistic graphical models more generally). Intelligent systems based on Bayesian networks are currently being used in a number of real-world applications including diagnosis, bioinformatics, channel coding, computer vision, text and social-network analysis, and data mining.
The objective of this class is to provide an in-depth exposition of knowledge representation, reasoning, and machine learning under uncertainty using the framework of Bayesian networks. Both theoretical underpinnings and practical considerations will be covered, with a special emphasis on constructing and learning graphical models, and on various exact and approximate inference algorithms. Additional topics include logical approaches to probabilistic inference, compilation techniques, sensitivity analysis, undirected graphical models, and statistical relational learning.
- 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.
Grading will be based on Homework (30%), Midterm (30%) and Final (40%). Midterm and final will be closed book.
There will be 5 homeworks, which will all be announced on CCLE. You will have at least a week to complete them.
Adnan Darwiche. Modeling and Reasoning with Bayesian Networks. Cambridge University Press 2009.
- Judea Pearl, Probabilistic Reasoning in Intelligent Systems. Morgan Kaufman 1988.
- Finn V. Jensen and Thomas Nielsen. Bayesian Networks and Decision Graphs. Springer 2007.
- Stuart Russell and Peter Norvig. Artificial Intelligence: A Modern Approach (AIMA). Prentice Hall 2009.
- Daphne Koller and Nir Friedman. Probabilistic Graphical Models. MIT Press 2009.
- SamIam from UCLA: http://reasoning.cs.ucla.edu/samiam/
- GeNIe/SMILE from the University of Pittsburgh: http://genie.sis.pitt.edu/
- Hugin lite from Hugin: http://www.hugin.com
- Netica from Norsys: http://www.norsys.com