Artificial Intelligence (CS 5300/CS 6300)

Artificial Intelligence
CS 5300/CS 6300
Spring 2012

Schedule:	Tue/Thr 12:25-1:45pm
Location:	WEB 2230
Instructor:	Jur van den Berg
Textbook:	S. Russell, P. Norvig. Artificial Intelligence: a Modern Approach, 3rd edition.
Office Hours:	MEB 2196; Tue/Thr 1:45pm-3pm, or by appointment
Mailing list:	cs5300@list.eng.utah.edu -- Subscribe (but don't post)! teach-cs5300@list.eng.utah.edu -- Post (but don't subscribe)!
TA:	Yang Song (office hours: M/W 10:30am-11.30am, MEB 3421) yangsong@cs.utah.edu

Links: [Course information] [Homework] [Projects] [FAQ]

This syllabus is subject to change. In the readings, RN refers to Russell and Norvig, SB to Sutton and Barto

Date Topics Readings Due Notes

T 10 Jan Introduction to AI opt: RN 1.1,2

Agents

H 12 Jan Search I
Depth and breadth first search 3.1,3.3,3.4
opt: RN 3.2

T 17 Jan Search II
A* Search and Heuristics 3.5-3.6
opt: RN 4.1-4.2

H 19 Jan Search III
Anytime A* Search ANA* paper P0

T 24 Jan Constraint Satisfaction I
Search and iterative algorithms RN 6-6.3 HW01

H 26 Jan Constraint Satisfaction II
Tree-structured CSPs RN 6.4-6.5

T 31 Jan Game Playing I
Minimax search RN 5-5.3 HW02

H 2 Feb Game Playing II
Expectimax search RN 5.4-5.5 P1

T 7 Feb Utility
Consistency and risk 16-16.3
opt: RN 16.4 HW03

Reinforcement Learning

H 9 Feb Markov Decision Processes I
Value iteration RN 17.1-2
SB 3, 4.4

T 14 Feb Markov Decision Processes II
Policy iteration RN 17.3
SB 4.1-3 HW04

H 16 Feb Reinforcement Learning I
TD-learning RN 21.1-2
SB 6.1-4

T 21 Feb Reinforcement Learning II
Q-learning RN 21.3
SB 6.5 HW05

H 23 Feb Reinforcement Learning III
Functional approximation RN 21.4-5
SB 8.1,8.2 P2

T 28 Mar Midterm review

H 1 Mar Midterm Exam

Reasoning Under Uncertainty

T 6 Mar Probability
Everything you need to know! RN 13-13.5
opt: RN 13.6

H 8 Mar Bayes' Nets I
Representation RN 14-14.4 P3

12-17 Mar Spring break

T 20 Mar Bayes' Nets II
Independence RN 14.4 HW06

H 22 Mar Bayes' Nets III
Inference RN 14.5

T 27 Mar Bayes' Nets IV
Sampling RN 14.5

H 29 Mar Decision Diagrams, HMMs
VPI, filtering RN 16.5-6, 15.1-3

T 3 Apr HMMs II
Smoothing, Viterbi algorithm RN 15.2 HW07

H 5 Apr HMMs III
Particle filtering RN 15.5

T 10 Apr Continuous domain
Gaussian distributions

Applications

H 12 Apr Machine Learning
Classification RN 18.1-2, 18.6-7, 18.9, 20.1-2, 22.1 P4

T 17 Apr POMDPs
Agents under uncertainty RN 17.4
Dummies, Sec 3-6 HW08

H 19 Apr POMDPs
Continuous POMDPs

T 24 Apr Final Exam Review

W 2 May FINAL EXAM
1pm - 3pm (WEB 2230) P5

Date	Topics	Readings	Due	Notes
T 10 Jan	Introduction to AI	opt: RN 1.1,2
Agents
H 12 Jan	Search I Depth and breadth first search	3.1,3.3,3.4 opt: RN 3.2
T 17 Jan	Search II A* Search and Heuristics	3.5-3.6 opt: RN 4.1-4.2
H 19 Jan	Search III Anytime A* Search	ANA* paper	P0
T 24 Jan	Constraint Satisfaction I Search and iterative algorithms	RN 6-6.3	HW01
H 26 Jan	Constraint Satisfaction II Tree-structured CSPs	RN 6.4-6.5
T 31 Jan	Game Playing I Minimax search	RN 5-5.3	HW02
H 2 Feb	Game Playing II Expectimax search	RN 5.4-5.5	P1
T 7 Feb	Utility Consistency and risk	16-16.3 opt: RN 16.4	HW03
Reinforcement Learning
H 9 Feb	Markov Decision Processes I Value iteration	RN 17.1-2 SB 3, 4.4
T 14 Feb	Markov Decision Processes II Policy iteration	RN 17.3 SB 4.1-3	HW04
H 16 Feb	Reinforcement Learning I TD-learning	RN 21.1-2 SB 6.1-4
T 21 Feb	Reinforcement Learning II Q-learning	RN 21.3 SB 6.5	HW05
H 23 Feb	Reinforcement Learning III Functional approximation	RN 21.4-5 SB 8.1,8.2	P2
T 28 Mar	Midterm review
H 1 Mar	Midterm Exam
Reasoning Under Uncertainty
T 6 Mar	Probability Everything you need to know!	RN 13-13.5 opt: RN 13.6
H 8 Mar	Bayes' Nets I Representation	RN 14-14.4	P3
12-17 Mar	Spring break
T 20 Mar	Bayes' Nets II Independence	RN 14.4	HW06
H 22 Mar	Bayes' Nets III Inference	RN 14.5
T 27 Mar	Bayes' Nets IV Sampling	RN 14.5
H 29 Mar	Decision Diagrams, HMMs VPI, filtering	RN 16.5-6, 15.1-3
T 3 Apr	HMMs II Smoothing, Viterbi algorithm	RN 15.2	HW07
H 5 Apr	HMMs III Particle filtering	RN 15.5
T 10 Apr	Continuous domain Gaussian distributions
Applications
H 12 Apr	Machine Learning Classification	RN 18.1-2, 18.6-7, 18.9, 20.1-2, 22.1	P4
T 17 Apr	POMDPs Agents under uncertainty	RN 17.4 Dummies, Sec 3-6	HW08
H 19 Apr	POMDPs Continuous POMDPs
T 24 Apr	Final Exam Review
W 2 May	FINAL EXAM 1pm - 3pm (WEB 2230)		P5