CS 440

Schedule

Date	Topic	Reading	Slides
8/22	Course overview, what is AI?	Russel and Norvig, Chapter 1	[pdf]
8/24	Intelligent agents Intro to python	Chapter 2	Agents Python
8/29	Problem solving by searching	Chapter 3	[pdf]
8/31	Informed search	Chapter 4	[pdf]
9/5	Local search	Chapter 4	[pdf]
9/7	Constraint satisfaction	Chapter 5	[pdf]
9/12	Constraint satisfaction (cont)
9/14	Games	Chapter 6	[pdf]
9/19	Propositional logic	Chapter 7	[pdf]
9/21	Proof methods for propositional logic	Chapter 7	[pdf]
9/26	First order logic	Chapter 8	[pdf]
9/28	Proof methods for first order logic	Chapter 9	[pdf]
10/3	Prolog	Chapter 9	[pdf]
10/5	Prolog (cont)		[pdf]
10/10	Prolog (cont)
10/12	Midterm
10/17	Proposal presentations
10/19	Proposal presentations (cont)
10/24	Uncertainty	Chapter 13	[pdf]
10/26	Intro to Bayesian networks	Chapter 14	[pdf]
10/31	Exact inference in Bayesian networks	Chapter 14	[pdf]
11/2	Approximate and probabilistic inference in Bayesian networks	Chapter 14	[pdf]
11/7	Approximate and probabilistic inference in Bayesian networks (cont.) Learning Bayesian networks	Chapter 14
11/9	An application of Bayesian networks: prediction of protein interaction sites	H. Wang, E. Segal, A. Ben-Hur, D. Brutlag and D. Koller. Identifying protein-protein interaction sites on a genome-wide scale. Advances in Neural Information Processing Systems 17, 2004	link to the paper.
11/14	Introduction to machine learning	Chapter 18	[pdf]
11/16	Decision trees	Chapter 18	[pdf]
11/28	Maximum likelihood and the Naive Bayes classifier	Chapter 20	[pdf]
11/30	Classifier assessment	Chapter 18	[pdf]