Schedule

This schedule is subject to change as the semester progresses, but it will be kept up to date. Slides are linked from the lecture title. They will be made available on the day of the lecture.

wk	Date	Topic	Readings & Milestones
1	Wed, Jan 5	Introduction: What is AI? (pre-recorded)	P&M chapter 1
1	Fri, Jan 7	Introduction: Representational dimensions	P&M chapter 1
2	Mon, Jan 10	Search: Graph search	P&M §3.1–3.4
2	Wed, Jan 12	Search: Uninformed Search	P&M §3.5
2	Fri, Jan 14	Search: Optimality & Simple Heuristic Search	P&M §3.6
3	Mon, Jan 17	Search: Heuristic Search & A*	P&M §3.7–3.8
3	Wed, Jan 19	Search: Branch & Bound	P&M §3.7–3.8 Assignment 1 released
3	Fri, Jan 21	Search: Local search	P&M §4.7
4	Mon, Jan 24	Uncertainty: Probability theory	P&M §8.1\
4	Wed, Jan 26	Uncertainty: Conditional independence	P&M §8.2
4	Fri, Jan 28	Uncertainty: Belief networks	P&M §8.3
5	Mon, Jan 31	Uncertainty: Independence in belief networks	P&M §8.4
5	Wed, Feb 2	Uncertainty: Inference in belief networks	P&M §8.4
5	Fri, Feb 4	Causality: Causal inference	Bar §3.4 Assignment 1 due
6	Mon, Feb 7	Supervised learning: Intro	P&M §7.1–7.3
6	Wed, Feb 9	Supervised learning: Linear Models	P&M §7.1–7.3
6	Fri, Feb 11	Supervised learning: Overfitting	P&M §7.4 Assignment 2 released
7	Mon, Feb 14	Supervised learning: Exact Bayesian models	P&M §10.4
7	Wed, Feb 16	Supervised learning: Monte Carlo estimation	P&M s.8.6
7	Fri, Feb 18	Deep learning: Calculus refresher	GBC §4.1, 4.3
	Mon, Feb 21	Winter Reading Break, NO CLASS
	Wed, Feb 23	Winter Reading Break, NO CLASS
	Fri, Feb 25	Winter Reading Break, NO CLASS
8	Mon, Feb 28	Deep learning: Neural networks	GBC §6.0–6.4.1 Assignment 2 due
8	Wed, Mar 2	Deep learning: Training Neural Networks	GBC §6.5
8	Fri, Mar 4	Deep learning: Convolutional neural networks	GBC §9.0–9.4
9	Mon, Mar 7	Deep learning: Recurrent neural networks	GBC §10.0–10.2, 10.4, 10.10 Assignment 3 released
9	Wed, Mar 9	Midterm review
9	Fri, Mar 11	Midterm exam
10	Mon, Mar 14	Reinforcement learning: Markov decision processes	S&B §3.0–3.4
10	Wed, Mar 16	Reinforcement learning: Policies and value functions	S&B §3.5
10	Fri, Mar 18	Reinforcement learning: Optimality and Dynamic Programming	S&B §3.6, §4.0–4.2
11	Mon, Mar 21	Reinforcement learning: Policy Iteration & Monte Carlo Prediction	S&B §4.3–4.4, §5.0–5.2
11	Wed, Mar 23	Reinforcement learning: Monte Carlo Control	S&B §5.3–5.5, §5.7
11	Fri, Mar 25	Reinforcement learning: Temporal difference learning	S&B §6.0–6.5 Assignment 3 due
12	Mon, Mar 28	Reinforcement learning: Function approximation	S&B §9.0–9.5.4 Assignment 4 released
12	Wed, Mar 30	Reinforcement learning: Policy gradient	S&B §13.0–13.3
12	Fri, Apr 1	Multiagent systems: Game theory for single interactions	S&LB §3.0–3.3.2
13	Mon, Apr 4	Multiagent systems: Game theory for sequential interactions	S&LB §5.0–5.2.2
13	Wed, Apr 6	Multiagent systems: Zero-sum games	S&LB §3.4.1
13	Fri, Apr 8	Final exam review
	Wed, Apr 13	Final exam	eClass
	Fri, Apr 15	Assignment 4 due