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	OUT	IN
1. Uninformed Search	08-26	Introduction/Syllabus	CH 1	HW1
	08-28	Uninformed Search (.pdf) Search Activity (.pdf)	videos CH 3-3.5.2, 3.7.1-3.7.2		Reading Quiz Canvas Background Survey
	08-30	Minimum Cost Search (.pdf) Exercise (.pdf) Complexity Theory	CH 3.5.3 videos http://en.wikipedia.org/wiki/NP-complete Python Basics Tutorial	HW2
2. Informed Search	09-02	Discuss Turing Paper	Computing Machinery and Intelligence	PA1	HW1
	09-04	Informed Search A* Activity (.pdf)	videos CH 3.6
	09-06	Wrap-Up Search	CH 3.7.3, 3.7.5 CH 3.7.6: Optimality of A*		HW2
3. Adversarial Search	09-09	CSP's CSP Activity	CH 4-4.5 Skim CH 4.6-4.7
	09-11	Minimax Algorithm (.pdf) Minimax Exercises (.pdf)	CH 10.2-10.2.2,10.3
	09-13	Expectimax + Evaluation Functions MIT AI:6.034 Games and Utility Handout (Exercises 1 and 3)	CH 6.1.4 (s)	Poster Project
4. Propositional Logic	09-16	Propositional Logic Propositional Logic Exercises (.pdf)	CH 5-5.2.2		PA1A
	09-18	Propositional Resolution resolution exercises (.pdf)	Automatic Theorem Proving
	09-20	Exam Review
5. Predicate Logic	09-23	EXAM 1
	09-25	First Order Logic + Datalog Predicate Logic Exercises (.pdf)	Predicate Logic Review (section 4) (s) CH 12-12.3
	09-27	Continue First Order Logic		PA2	PA1B
6. Machine Learning	09-30	First Order Theorem Proving Proof Exercises (.pdf)	CH 12.4 Resolution Tutorial	Proof HW (2-4)
	10-02	Linear Algebra Linear Algebra Exercises (.pdf)	Linear Algebra Review and Reference (.pdf) sections 1-3.7, (skip 3.6)
	10-04	Numpy Numpy Exercises (.pdf) numpy_exercises.py	Numpy Tutorial		Proof HW
7. Neural Networks	10-07	Gradient Descent + Linear Regression (.pdf) Linear Regression Exercise (.pdf)	CH 4.10.2, 7-7.2.1, 7.3.2 Calculus Refresher (.pdf) (9-13) (s)
	10-09	Neural Networks (.pdf)	CH 7.4.1
	10-11	single_layer_nn.py usps.npy	CH 7.5-7.6 (skip 7.5.1)
8. Misc. Learning	10-14	Support Vector Machines	What is a Support Vector Machine? Hype or Hallelujah (s)
	10-16	K-Nearest-Neighbors (.pdf)	CH 7.6		PA2
	10-18	Start PA3	Kaggle Exercise makeSubmission.py	PA3	Poster Proposal
9. Probabilistic AI	10-21	Probability + Bayes Rule (.pdf) probability exercises (.pdf)	CH 6.1-6.2
	10-23	Belief Nets + Naive Bayes (.pdf)	CH 6.3
	10-25	Bayes classifier exercises (.pdf)
10. Markov Models	10-28	Review Session
	10-30	EXAM 2
	11-01	Markov Models Markov Model Exercises (.pdf)	CH 6.5-6.5.2
11. Reinforcement Learning	11-04	Hidden Markov Models and Dynamic Belief Networks Berkeley CS188 Lecture Slides (.pdf) HMM Exercises (.pdf)	CH 6.5.3-6.6 CH 6.4.2
	11-06	Markov Decision Processes Berkely CS188 Lecture Slides (.pdf) Value Iteration Demo MDP Exercises (.pdf)	CH 9.5-9.5.2		PA3
	11-08	Continue MDP's
12. Genetic Algorithms	11-11	Q-Learning Q-Learning Demo Q-Learning Exercises (.pdf)	CH 11.3-11.3.3	PA4	Poster Bibliography
	11-13	Work Day
	11-15	Local Search andGenetic Algorithms (.pdf) GA Demo	CH 4.8-4.9
13. Ethical Issues	11-18	Approximate RL Approximate RL Exercises (.pdf)
	11-20	Ethics and AI
	11-22	Review Session
14. Thanksgiving	11-25	THANSKGIVING
	11-27	THANSKGIVING
	11-29	THANSKGIVING
15. Poster Session and Review	12-02	Poster Session
	12-04	Poster Session
	12-06				PA4