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 11	Introduction: What is AI?	P&M chapter 1
1	Wed, Jan 13	Introduction: Representational dimensions	P&M chapter 1
1	Fri, Jan 15	Search: Graph search	P&M §3.1–3.4
2	Mon, Jan 18	Search: Uninformed Search	P&M §3.5
2	Wed, Jan 20	Search: Uninformed Search II; Heuristic search	P&M §3.6
2	Fri, Jan 22	Search: Heuristic Search II	P&M §3.7–3.8
3	Mon, Jan 25	Search: Branch & Bound	P&M §3.7–3.8 Assignment 1 released
3	Wed, Jan 27	Search: Local search	P&M §4.7
3	Fri, Jan 29	Uncertainty: Probability theory	P&M §8.1
4	Mon, Feb 1	Uncertainty: Conditional independence	P&M §8.2
4	Wed, Feb 3	Uncertainty: Belief networks	P&M §8.3
4	Fri, Feb 5	Uncertainty: Independence in belief networks	P&M §8.4
5	Mon, Feb 8	Uncertainty: Inference in belief networks	P&M §8.4 Assignment 1 due
5	Wed, Feb 10	Causality: Causal inference	Bar §3.4
5	Fri, Feb 12	Supervised learning: Intro	P&M §7.1–7.3 Assignment 2 released Add/Drop deadline
	Mon, Feb 15	Winter Reading Break, NO CLASS
	Wed, Feb 17	Winter Reading Break, NO CLASS
	Fri, Feb 19	Winter Reading Break, NO CLASS
6	Mon, Feb 22	Supervised learning: Linear Models	P&M §7.1–7.3
6	Wed, Feb 24	Supervised learning: Overfitting	P&M §7.4
6	Fri, Feb 26	Supervised learning: Exact Bayesian models	P&M §10.4
7	Mon, Mar 1	Deep learning: Calculus refresher	GBC §4.1, 4.3
7	Wed, Mar 3	Deep learning: Neural networks	GBC §6.0–6.4.1
7	Fri, Mar 5	Deep learning: Training Neural Networks	GBC §6.5 Assignment 2 due
8	Mon, Mar 8	Deep learning: Convolutional neural networks	GBC §9.0–9.4
8	Wed, Mar 10	Deep learning: Recurrent neural networks	GBC §10.0–10.2, 10.4, 10.10 Assignment 3 released
8	Fri, Mar 12	Midterm review
9	Mon, Mar 15	Midterm exam
9	Wed, Mar 17	Reinforcement learning: Markov decision processes	S&B §3.0–3.4
9	Fri, Mar 19	Reinforcement learning: Policies and value functions	S&B §3.5
10	Mon, Mar 22	Reinforcement learning: Optimality and Dynamic Programming	S&B §3.6, §4.0–4.2
10	Wed, Mar 24	Reinforcement learning: Policy Iteration	S&B §4.3–4.4
10	Fri, Mar 26	Reinforcement learning: Monte Carlo Prediction	S&B§5.0–5.2
11	Mon, Mar 29	Reinforcement learning: Monte Carlo Control	S&B §5.3–5.5, §5.7 Assignment 3 due
11	Wed, Mar 31	Reinforcement learning: Temporal difference learning	S&B §6.0–6.5
	Fri, Apr 2	Good Friday, NO CLASS
	Mon, Apr 5	Easter Monday, NO CLASS
12	Wed, Apr 7	Reinforcement learning: Function approximation	S&B §9.0–9.5.4
12	Fri, Apr 9	Reinforcement learning: Policy gradient	S&B §13.0–13.3 Assignment 4 released
13	Mon, Apr 12	Multiagent systems: Game theory for single interactions	S&LB §3.0–3.3.2
13	Wed, Apr 14	Multiagent systems: Game theory for sequential interactions	S&LB§5.0–5.2.2
13	Fri, Apr 16	Final exam review
	Mon, Apr 19	Assignment 4 due
	Wed, Apr 28	Final exam	eClass