The schedule below represents my current best estimate concerning due dates (and everything else). I am providing this information to give you a general idea of the pace and timing of the class. This schedule will certainly change as the semester progresses.

Unless otherwise noted, all readings are from

• Artificial Intelligence: Foundations of Computational Agents, David Poole and Alan Mackworth, Cambridge University Press, 2010.

Readings followed by (S) are supplemental. You should read them, but you won't be tested on that material.

WEEK	DATE	TOPIC	READING/VIDEOS	OUT	IN
1. Uninformed Search	08-25	Introduction/Syllabus	CH 1	HW1
	08-27	Uninformed Search (.pdf) search_exercises.pdf (In ISAT/CS 143)	CH 3-3.5.2, 3.7.1-3.7.2 Search Videos	PA1	Reading Quiz Background Survey (Canvas)
	08-29	Minimum Cost Search Complexity Theory Minimum Cost Exercises	CH 3.5.3 http://en.wikipedia.org/wiki/NP-complete Videos
2. Informed Search	09-01	Discuss Turing Paper	Computing Machinery and Intelligence		HW1
	09-03	Best First Search A* astar_activity.pdf	CH 3.6, CH 3.7.3, 3.7.5 "Optimality of A*" A* Videos	PA2	PA1
	09-05	Minimax (.pdf) minimax_activity.pdf	CH 10.2-10.2.2,10.3 Minimax Videos
3. Adversarial Search	09-08	Expectimax + Evaluation Functions MIT AI:6.034 Games and Utility Handout (Exercises 1 and 3)	CH 6.1.4 (s) Videos	Poster Project
	09-10	Constraint Satisfaction Problems (In ISAT/CS 143)	CH 4-4.5 Skim CH 4.6-4.7 CSP Videos
	09-12	Propositional Logic	CH 5-5.2.2 Propositional Logic Videos		PA2A
4. Propositional Logic	09-15	No Class CONFERENCE
	09-17	Prep for Review CONFERENCE
	09-19	Exam Review CONFERENCE
5. Predicate Logic	09-22	Propositional Resolution resolution_exercises.pdf	Automatic Theorem Proving Propositional Resolution Videos
	09-24	Midterm #1
	09-26	Predicate Logic	Predicate Logic Review (section 4) (s) CH 12-12.3.1 Predicate Logic Video	PA3	PA2B
6. Machine Learning	09-29	Inference in Predicate Logic pred_logic_proofs.pdf pred_logic_proofs.tex	CH 12.3.2-12.4 Resolution Tutorial Videos
	10-01	Linear Algebra (.pdf) linear_algebra_exercises.pdf	Linear Algebra Review and Reference (.pdf) sections 1-3.7, (skip 3.6) Linear Algebra Video
	10-03	Gradient Descent +Linear Regression (.pdf)	CH 4.10.2, 7-7.2.1, 7.3.2 Calculus Refresher (.pdf) (9-14) (s) Videos
7. Neural Networks	10-06	Numpy exercises (.pdf) numpy_exercises.py	Numpy Tutorial		Proof HW
	10-08	Classification (.pdf) single_layer_network.py usps.npy	CH 7.3.2 Videos
	10-10	Multi-layer Neural Networks (.pdf)	CH 7.4.1 Videos
8. Misc. Learning	10-13	Validation (.pdf) K-Nearest Neighbors (.pdf)	7.5 (skip 7.5.1) 7.6 Videos
	10-15	Work Day	CH 7.6		PA3
	10-17	Support Vector Machines lectures/svm/svm_demos.py	What is a Support Vector Machine? Support Vector Machines: Hype or Halleluja? (s)		Poster Proposal
9. Probabilistic AI	10-20	Start PA4	Kaggle Python Tutorial	PA4
	10-22	Probability + Bayes Rule probability_review.pdf	CH 6.1-6.2 Video
	10-24	Belief Nets + Naive Bayes (.pdf)	CH 6.3, 7.3.3
10. Markov Models	10-27	Review Session naive_bayes_exercise.pdf
	10-29	EXAM 2
	10-31	Markov Models (.pdf) markov_model_exercises.pdf	CH 6.5-6.5.2 Video
11. Reinforcement Learning	11-03	Hidden Markov Models and Dynamic Belief Networks filtering_exercise.pdf	CH 6.5.3-6.6 Videos
	11-05	Markov Decision Processes mdp_exercises.pdf Value Iteration Demo	CH 9.5-9.5.2 Video
	11-07	Continue MDP's			PA4
12. Genetic Algorithms	11-10	Q-Learning Q-Learning Demo q_learning_activity.pdf	CH 11.3-11.3.5 Video	PA5	Poster Bibliography
	11-12	Local Search and Genetic Algorithms GA Demo	CH 4.8-4.9
	11-14	CONFERENCE
13. Poster Sessions	11-17	Approximate Q-Learning Activity (.pdf)			Poster Submission
	11-19	Poster Session
	11-21	Poster Session
14. Thanksgiving	11-24	THANSKGIVING
	11-26	THANSKGIVING
	11-28	THANSKGIVING
15. Ethical Issues and Review	12-01	K-Means and EM (.pdf)	CH 11-11.1.3 Videos
	12-03	Ethics and AI	The Ethics of Artificial Intelligence (.pdf) Humans Need Not Apply (video)
	12-05	Review Session			PA5
	12-10	Final Exam 1:00-3:00PM