s## 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	Mon, Jan 7	Introduction: What is AI?	P&M chapter 1 NO LAB THIS WEEK
1	Wed, Jan 9	Introduction: Representational dimensions	P&M chapter 1
1	Fri, Jan 11	Search: Graph search	P&M §3.1–3.4
2	Mon, Jan 14	Search: Uninformed search	P&M §3.5 NO LAB THIS WEEK
2	Wed, Jan 16	Search: Heuristic search	P&M §3.6
2	Fri, Jan 18	Search: Branch & bound	P&M §3.7–3.8 Add/Drop deadline
3	Mon, Jan 21	Search: Local search	P&M §4.7 Assignment 1 released
3	Wed, Jan 23	Uncertainty: Probability theory	P&M §8.1
3	Fri, Jan 25	Uncertainty: Conditional independence	P&M §8.2
4	Mon, Jan 28	Uncertainty: Belief networks	P&M §8.3
4	Wed, Jan 30	Uncertainty: Inference in belief networks	P&M §8.4
4	Fri, Feb 1	Uncertainty: Independence in belief networks & Causality: Intro	P&M §8.4, Bar §3.4
5	Mon, Feb 4	Causality: Causal inference	Bar §3.4 Assignment 1 due
5	Wed, Feb 6	Supervised learning: Intro	P&M §7.1–7.2
5	Fri, Feb 8	Supervised learning: Linear models	P&M §7.3 Assignment 2 released
6	Mon, Feb 11	Supervised learning: Overfitting	P&M §7.4
6	Wed, Feb 13	Supervised learning: Exact Bayesian models	P&M §10.4
6	Fri, Feb 15	Supervised learning: Monte Carlo estimation	P&M §8.6
	Mon, Feb 18	Winter Reading Break, NO CLASS
	Wed, Feb 20	Winter Reading Break, NO CLASS
	Fri, Feb 22	Winter Reading Break, NO CLASS
7	Mon, Feb 25	Deep learning: Calculus refresher	GBC §4.1, 4.3
7	Wed, Feb 27	Deep learning: Neural networks	GBC §6.0–6.4.1
7	Fri, Mar 1	Deep learning: Convolutional neural networks	GBC §9.0–9.4
8	Mon, Mar 4	Deep learning: Recurrent neural networks	GBC §10.0–10.2, 10.4, 10.10 Assignment 2 due
8	Wed, Mar 6	Deep learning: Autoencoders	GBC §14.0–14.5
8	Fri, Mar 8	Midterm review	Assignment 3 released
9	Mon, Mar 11	Midterm exam
9	Wed, Mar 13	Reinforcement learning: Markov decision processes	S&B §3.0–3.4
9	Fri, Mar 15	Reinforcement learning: Policies and value functions	S&B §3.5
10	Mon, Mar 18	Reinforcement learning: Optimality and Policy Evaluation	S&B §3.6, §4.0–4.2
10	Wed, Mar 20	Reinforcement learning: Policy Iteration and Monte Carlo Prediction	S&B §4.3–4.4, §5.0–5.2
10	Fri, Mar 22	Reinforcement learning: Monte Carlo Control	S&B §5.3–5.5, §5.7
11	Mon, Mar 25	Reinforcement learning: Temporal difference learning	S&B §6.0–6.5 Assignment 3 due
11	Wed, Mar 27	Reinforcement learning: Function approximation	S&B §9.0–9.5.4
11	Fri, Mar 29	Reinforcement learning: Policy gradient	S&B §13.0–13.3
12	Mon, Apr 1	Multiagent systems: Game theory for single interactions	S&LB §3.0–3.3.2 Assignment 4 released
12	Wed, Apr 3	Multiagent systems: Game theory for sequential interactions	S&LB §5.0–5.2.2
12	Fri, Apr 5	Multiagent systems: Zero-sum games	S&LB §3.4.1
13	Mon, Apr 8	Multiagent systems: Aggregating preferences	S&LB §9.1–9.4, §10.1–10.3
13	Wed, Apr 10	Final exam review
	Fri, Apr 12	NO CLASS	Assignment 4 due
	Wed, Apr 24	Final exam	CSC B2 (usual location)