CMSC 471, Fall 2009 - Course Syllabus
as of 8/28/09

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Class

Date

Topic

Reading

Homework

Comments

1 Tu 9/1 Course overview; What is AI? Ch. 1, Lisp Ch. 1, McCarthy paper Pretest and HW1(P) out Slides
2 Th 9/3 Agents/Lisp Ch. 2, Lisp Ch. 2-3, Graham article
Slides
Lisp debugging handout
Fibonacci example handout
Emacs reference card
3 Tu 9/8 Problem solving as search; Lisp Ch. 3.1-3.3, Lisp Ch. 4-5, App. A Pretest due Slides
4 Th 9/10 Uninformed search Ch. 3.4-3.7
(see 9/8 for slides); example from class: neg.lisp; example from Lisp session: graph.lisp; graph1.lisp; graph2.lisp
5 Tu 9/15 Informed search Ch. 4.1-4.2, Lisp Ch. 7 HW1 due; HW2(PW) out
Slides
6 Th 9/17 Local search, genetic algorithms Ch. 4.3,4.5-4.6 Student evaluation #1 (see 9/15 for slides); words.lisp;
7 Tu 9/22 Constraint satisfaction Ch. 5
Dr. desJardins out of town; Guest lecturer Don Miner; Slides
8 Th 9/24 Game playing Ch. 6.1-6.2
Dr. desJardins out of town; Guest lecturer Don Miner; Slides
9 Tu 9/29 Game playing II Ch. 6.3-6.8
(See 9/24 for slides)
10 Th 10/1 Knowledge-based agents; project overview Ch. 7 Project teams formed; Project description out; HW2 due; HW3(W) out Slides
11  Tu 10/6 Propositional logic (review)

  Slides
12 Th 10/8 First-order logic Ch. 8   Slides; Mastermind project description; mm.lisp; mm-solver.lisp
13 Tu 10/13 Logical inference Ch.9
Slides
14
Th 10/15
Philosophy and history of AI
Ch. 26, 27, Turing article; Searle article; Three Laws of Robotics (Wikipedia) HW3 due; HW4(PW) out
Chronology of AI
15 Tu 10/20 State-space and partial-order planning
Ch. 10.3
Slides
16 Th 10/22 Partial-order and hierarchical planning Ch. 11.1-11.3, 12.2   (See 10/20 for slides)
17 Tu 10/27 MIDTERM (covers material through class #14)



18 Th 10/29 Probabilistic reasoning
Ch. 13
Student evaluation #2; HW4 due; HW5(W) out Slides
19 Tu 11/3 Bayesian networks

Ch. 14 Project design due Slides
20 Th 11/5 Machine learning I: decision trees Ch. 18.1-18.3
Guest lecturer: Denise Rockwell; Slides
21 Tu 11/10 Machine learning II: k-nearest neighbor, naive Bayes, learning Bayes nets Ch. 20.1-20.4
train-biases.lisp, Bias #1 training data, Bias #2 training data, Bias #3 training data; Slides
22 Th 11/12 Machine learning III: neural networks, support vector machines, clustering Ch. 20.5-20.8 HW5 due; HW6(W) out
Slides
23 Tu 11/17 Markov decision processes; probabilistic planning Ch. 15.1, 16.1-16.3, 17.1-17.2   Tournament dry run #1: Fixed-size and scalability challenges; Slides
25 Th 11/19 Graduate Student Research Day
   
24 Tu 11/24 Reinforcement learning Ch. 21.1-21.3
Basic RL slides; TD slides

Thu 11/26 Happy Thanksgiving -- enjoy your turkey!
26 Tu 12/1
Multi-agent systems I
Ch. 16.4, 17.6-17.7
Slides
Data for test biased choosers and probabilistic choosers (#4): test-bias1.txt; test-bias2.txt; test-bias3.txt; test-bias4.txt; train-bias4.txt; train-bias4.lisp
27 Th 12/3 Multi-agent systems II


Slides: See 12/1
28 Tu 12/8 Singularity Debate
HW6 due
Tournament dry run #2: Learning challenge
29 Th 12/10 Tournament
Tournament
-- Th 12/17 FINAL EXAM (10:30-12:30, NOT 12:30-2:30 as previously posted!)   Project and final report due