CSE515 Statistical Methods in Computer Science – Spring 2011

Lectures (tentative)

Week	Date	Topic	Reading	Slides	HW
1	3/28	Introduction, Bayesian network representation	2.1-3, 3.1	(PDF) (Annotated PDF)
	3/30	Bayesian network representation cont.	3.1-3	(PDF) (Annotated PDF)
2	4/4	Local probability models	5	(PDF)	HW1 out
	4/6	Undirected graphical models I	4	(PDF) (Annotated PDF)
3	4/11	Undirected graphical models II + CRFs and applications	4	(PDF) (Annotated PDF)
	4/13	Inference: Exact inference	9.1-3	(PDF) (Annotated PDF)
4	4/18	Exact inference	9.4-6	(PDF) (Annotated PDF)	HW2 out; HW1 due
	4/20	Exact inference: Clique Trees	10.1-4	(PDF) (Annotated PDF)
5	4/25	Learning: parameter estimation	17	(PDF) (Annotated PDF)
	4/27	Parameter learning in BNs	17	(PDF) (Annotated PDF)
6	5/2	Structure learning in BNs	18	(PDF) (Annotated PDF)	HW3 out; HW2 due
	5/4	Partially observed data (learning with missing data)	19	(PDF) (Annotated PDF)
7	5/9	EM algorithm and Applications		(PDF) (Annotated PDF)
	5/11	Approximate inference: particle based	12	(PDF) (Annotated PDF)
8	5/16	Particle-based approximate inference	12	(PDF) (Annotated PDF)	HW3 due
	5/18	Global approximate inference	11	(PDF) (Annotated PDF)	HW4 out
9	5/23	Learning Undirected Models	20	(PDF) (Annotated PDF)
	5/25	Markov Decision Processes (Instructor: Mausam)		(PDF)
10	5/30	(memorial day)
	6/1	Temporal models (DBNs, HMMs)		(PDF)	HW4 due