Artificial Intelligence, Winter, 2010-2011

Course Text: Artificial Intelligence: A Modern Approach, 3rd Ed., by Russell and Norvig.

Grading Scheme:

• 20% Problem Sets (≈ 4)
• 20% Programming Projects (2)
• 25% Midterm
• 35% Final

errata
Hw 1, Problem 3, should read: Lugoj to Bucharest
HW 3, Problem 2, The second constraint should read |r_i-r_j| != |i-j|, rather than |r_i-r_j| != |r_i-r_j| (which is impossible).

Projects:

• Project 1
• Project 2

Lectures:

Date	Topic	Reading	Notes
Mon, Nov. 29	Introduction and History	1.1, 1.3, 1.4, 1.5. Optional: Turing's 1950 paper
Wed, Nov. 30	Classical Search (BFS, DFS, DLS, Iterative Deepening)	3.1, 3.2, 3.3, 3.4	HW1 Assigned
Mon, Dec. 6	More Classical Search (A*, heuristic functions) Start Beyond Classical Search.	3.5, 3.6
Wed, Dec. 8	Python Tutorial (Guest Lecture by Haitao Du)		Example Code
Mon, Dec. 13	Local Search, Hill Climbing, Simulated Annealing, Local Beam Search	4.1, 4.1.1, 4.1.2, 4.1.3
Wed, Dec. 15	Genetic Algorithms, Start minimax, Local Search in Continuous Spaces	4.1.4, 5.1, 5.2, 4.2	HW1 Due, Proj 1 Assigned soon
Break
Mon, Jan. 3
Wed, Jan. 5	Finish Local Search, Finish minimax & alpha-beta pruning
Mon, Jan. 10	Constraint Satisfaction	Chapter 6	5.4.1, 5.4.2
Wed, Jan. 12	More Constraint Satisfaction, Review for Midterm		HW 2 due
Mon, Jan. 17	Midterm
Wed, Jan. 19	Review midterms. More constraint satisfaction.		Proj. 1 due
Mon, Jan. 24	Finish constraint satisfaction. Uncertainty	13.2, 13.2.1, 13.2.2, 13.2.3
Wed, Jan. 26	Start Bayes Nets	Chapter 14
Mon, Jan. 31	More Bayes Nets
Wed, Feb. 2	Start HMMs		HW 3 Due
Mon, Feb. 7	Mostly finish HMMs, Start Learning
Wed, Feb. 9	Two last HMM examples, Learning intro, Start decision trees.	Ch. 18
Mon, Feb. 14			Project 2 Due
Wed, Feb. 17
Fri, Feb. 25	Final Exam from 10:15am-12:15pm

TENTATIVE Schedule:

Topic	# Lectures	Book sections
Introduction, History	1	Chap. 1
Classical Search	2	Chap. 3
Python Tutorial	1
Beyond Classical Search	1.5	Chap. 4
Adversarial Search	2	Chap. 5
Prositional Logic	1.5	Chap. 7
First-Order Logic	1	Chap. 8
MIDTERM	1
Uncertainty	1	Chap. 13
Bayesian Networks	1.5	Chap. 14
Hidden Markov Models	1.5	Chap. 15
Learning Intro, Cross Validation	1.5	18.1, 18.4
Decision Trees, Evaluating Hypotheses	2	18.3, 18.4
Linear Methods & Perceptrons	1	18.6
Neural Networks and Backpropagation	1.5	18.7

Final Exam: Friday Feb. 25, 2011, 10:15am - 12:15pm (Location TBD)