CS 325 Artificial Intelligence

Semester Schedule

Class# Date Class Topic Textbook/Slides Videos Assignment Given Assignment Due
1 01/15 Course overview, grading, homeworks.
History of AI 		Ch 1
(slides) 		-
2 01/17 Intelligent Agents Ch 2
(PPT, PDF) 		1. Welcome to AI Student Survey
3 01/22 Problem Solving: Route Finding, Tree Search,
Graph Search, A* Search, State Spaces 		Ch 3
(PPT, PDF) 		2. Problem Solving HW #1
4 01/24 Probability in AI: Basics, Bayesian Nets, Conditional Probability Ch 13
(PPT, PDF) 		3. Probability in AI
5 01/29 Probabilistic Inference: Probability review, Bayes Nets, D-Separation Ch 14
(Slides, SRb) 		4. Probabilistic Inference
6 01/31 Probabilistic Inference (cont.): Enumeration, Sampling
Machine Learning I: Supervised, Decision Trees, Theory, Regression/Classification, ANNs, SVMs 		Ch 18
(Slides1, Slides2) 		5. Machine Learning HW #2 HW #1
7 02/05 Machine Learning I (cont) Chs 4+18
(Slides) 		(same)
8 02/07 Machine Learning II: Unsupervised learning, clustering, nerve gas, expectation maximization, independent component analysis Chs 18+20
(Slides) 		6. Unsupervised Learning
9 02/12 Knowledge, Reasoning: Propositional Logic, Truth Tables, First Order Logic Chs 7, 8, 9
(Slides) 		7. Representation with Logic HW #3 HW #2
10 02/14 Knowledge Representation: Ontologies, Semantic Web, Functional Programming, LISP, Haskell, binding problem Ch 9, 12
(Slides)
11 02/19 Planning: Problem Solving vs Planning, Sensorless, Partially Observable, Progression/Regression Search, Situation Calculus Ch 10, 11
(Slides) 		8. Planning HW #3
12 02/21 Planning under Uncertainty: Markov, Policy, Deterministic vs Stochastic, Partial Observability Ch 17
(Slides) 		9. Planning under Uncertainty
13 02/26 Reinforcement Learning: agents, Q-learning, exploration Ch 21
(Slides, survey) 		10. Reinforcement Learning HW #4
14 02/28 Hidden Markov Models, Particle Filtering Chs. 15,20
(Slides, survey) 		11. HMMs and Filters
03/05 MIDTERM (Study Guide)
15 03/07 Markov review: Deterministic, Convergence, Optimal Policy (Slides) 12. MDP Review HW #4
SPRING BREAK
16 03/19 Midterm Review (Solutions)
17 03/21 Games: Single-agent, Adversarial, Tree Search, Complexity, Stochastic Ch 5
(Slides, survey) 		13. Games
18 03/26 Game Theory: Dominant Strategy, Optimality Ch 17.5-6
(Slides, survey) 		14. Game Theory HW #5
19 03/28 Advanced Planning: Scheduling, Hierarchical, Refinement Ch. 11
(Slides, survey) 		15. Advanced Planning
20 04/02 Computer Vision I: Projection, Perspective, Invariance, Extracting Features, Line and Corner Detection Ch. 24
(Slides, survey) 		16. Computer Vision I HW #5 (ext.)
21 04/04 Computer Vision II: Depth, Stereo, Alignment Ch. 24
(Slides, survey) 		17. Computer Vision II
22 04/09 Computer Vision III: Motion Ch. 24
(Slides, survey) 		18. Computer Vision III HW #6 HW #5
23 04/11 Robotics I: Autonomous Vehicle, Robotics, Kinematics, Dynamic, Monte Carlo Ch. 25
(Slides, survey) 		19. Robotics I
24 04/16 Robotics II: Prediction, Measurement, Resampling, Planning, Dynamic Programming Ch. 25
(Slides, survey) 		20. Robotics II HW #6
25 04/18 Natural Language Processing I: Language Models, Bag of Words, Probabilistic Models, Learning, Unigram vs, Gzip, Segmentation, Spelling Correction Ch. 22
(Slides, survey) 		21. Natural Language Processing I
26 04/23 Natural Language Processing II: Sentence Structure, Parsing, Grammars, Machine Translation Ch. 23
(Slides) 		22. Natural Language Processing II
27 04/25 Review Session for Final Exam (Study Guide)

Final Exam Date: Thursday, May 2 4-7pm