Lectures

(Tentative Schedule)

See also the course calendar.

Date	Topic	Readings	Notes	Additional Resources
9/4	Introduction to AI	R&N Chapter 1	Slides
9/6	Blind Search	R&N 3.0-3.4	Notes	Blind Search Algorithms
9/9	Informed Search	R&N 3.4-3.5.1	Slides	Notes
9/11	A* Search	R&N 3.5.2-3.7	Notes	Pathfinding Visualizations
9/13	Adversarial Search	R&N 5.0-5.3	Notes
9/16	Local Search	R&N 4.0-4.1	Notes
9/18	Satisfiability	R&N 7.3-7.5.2	Notes
9/20	Constraint Satisfaction	R&N 6.1, 6.4	Notes
9/25	Probability Review	R&N 13	Notes
9/27	Bayesian Networks	R&N 14-14.5	Notes	Particle Filters
9/30	kNN and the Bias-Variance Tradeoff	R&N 18-18.2	Slides
10/2	Decision Trees	R&N 18.3	Notes
10/4	Naive Bayes	R&N 13.5-6, 20.1, 20.2.2	Notes
10/7	Continuous Optimization	R&N 18.6.1	Notes
10/9	Linear Algebra Primer	R&N A.2	Notes
10/11	Statistics for ML		Notes
10/16	Linear Regression	R&N 18.6	Notes
10/18	Statistics for ML, cont'd Linear Regression, cont'd	R&N 18.6	Notes Notes
10/21	Neural networks: Simple Perceptron	R&N 18.7-18.7.2	Notes (DRAFT)
10/23	Neural networks: Back-propagation	R&N 18.7.4	Notes
10/25	Deep Learning	R&N 18.7.4	Notes
10/28	Markov Chains	R&N 14.5.2	Notes
10/30	Markov Decision Processes: Prediction		Notes
11/1	Markov Decision Processes: Control		Notes
11/4	Reinforcement Learning		Notes
11/6	Reinforcement Learning, cont'd		Notes
11/8	RL with Function Approximation		Notes
11/11	Monte-Carlo Tree Search		Notes
11/13	Learning for Go		Notes
11/15	Eigenvalues & Eigenvectors		Notes
11/18	Principal Component Analysis		Notes
11/20	Clustering (k-means and EM)		Notes
11/22	Hidden Markov Models		Notes
11/25	Bonus Lecture: Amy + Eric's research		Notes
12/2	Guest Lecture: Randall Balestriero (Deep Learning)		Notes
12/4	Guest Lecture: James Tompkin (Vision)		Notes
12/6	Guest Lecture: Ellie Pavlick (NLP)		Notes