Below is the calendar for this semester course. This is the preliminary schedule, which will be altered as the semester progresses. I will attempt to announce any change to the class, but this webpage should be viewed as authoritative.

Module 1: Search

	Date	Lecture Topic	Readings	Homework	due	Project	due	Contest	due
H	Aug 24	Intro & Course Overview	Ch 1,2	HW0 (warm-up)	Aug 28
T	Aug 26	Uniformed Search	Ch 3.[1-4]	HW1 (search)	Sep 4	P0 (tutorial)	Aug 30
H	Aug 31	A* Search and Heuristics	Ch 3.[5-6]
T	Sep 2	Local Search	Ch 4	HW2 (CSP)	Sep 9	P1 (search)	Sep 14
H	Sep 5	CSP I	Ch 6.1
T	Sep 7	CSP II	Ch 6.[2-5]
H	Sep 9	Game Trees: Minimax	Ch 5.[2-4]	HW3 (games)	Sep 16			contest 1	Oct 18
T	Sep 14	Game Trees: Expectimax	Ch 5.[2-5], 16.[1-3]			P2 (MAS)	Sep 30
H	Sep 16	Review 1		Mini-exam 1	Sep 17-19

Module 2: Uncertainty

	Date	Lecture Topic	Readings	Homework	due	Project	due	Contest	due
T	Sep 21	Review of Probability	Ch 13.[1-5]	HW4 (BN I)	Oct 5 (ext)
H	Sep 23	Bayesian Networks I	Ch 14.[1-3]
T	Sep 28	Bayesian Networks II	Ch 14.[4-5]	HW5 (BN II)	Oct 7
H	Sep 30	Bayesian Networks III	Ch 14.[4-5]
T	Oct 5	Bayesian Networks IV	Ch 14.[4-5]
H	Oct 7	Hidden Markov Models	Ch 15.[2-5]	HW6 (HMM)	Oct 14	P3 (RL)	Nov 6
T	Oct 12	Markov Decision Processes I	Ch 17.[1-3]	HW7 (MDP)	Oct 18
H	Oct 14	Markov Decision Processes II	Ch 17.[1-3]
T	Oct 19	Review 2		Mini-exam 2	Oct 20-22

Module 3: Learning

	Date	Lecture Topic	Readings	Homework	due	Project	due	Contest	due
H	Oct 21	No Lecture (due to exam)
T	Oct 26	Reinforcement Learning I	Ch 17.[1-3]	HW8 (RL)	Nov 2
H	Oct 28	Reinforcement Learning II	Ch 17.[1-3]					C1 winners announc.
T	Nov 2	Naive Bayes	Ch 20.[1-2]	HW9 (ML1)	Nov 9	P4 (ML)	Nov 25	contest 2	Dec 1
H	Nov 4	Decision Trees	Ch 18.[1-3]
T	Nov 9	Neural Networks I	Ch 18.[8]	HW10 (NNs)	Nov 22
H	Nov 11	Neural Networks II	Ch 18.[7]
T	Nov 16	Deep Learning I	...
H	Nov 18	Deep Learning II
T	Nov 23	Final Review						C2 winners announc.
	Dec 1-4	(on Gradscope)		Final Exam	Dec 1-4