Video links

The lecture recordings of the courses in the course links, arranged by chapter. The reading plan gives the matching slides and notes.

Video or playlist	Link	Notes
UC Berkeley CS188, Spring 2024	Playlist	The numbers below are lecture numbers.
Stanford CS221, Autumn 2019	Individual lectures below	Factor graphs and logic.
CMU 10-708, Spring 2020	Lecture page	Each lecture links a recording; the numbers below are lecture numbers.
Brady Neal, Introduction to Causal Inference	Course page	Short videos for each week of the course.
Tim Roughgarden, Algorithmic Game Theory (Stanford CS364A, Fall 2013)	Course page	Twenty lectures, each linked from the course page.

By chapter

Chapter	CS188 Spring 2024 lectures	Other recordings
1. Agents and Uninformed Search	1: Intro to AI, rational agents · 2: State spaces, uninformed search	—
2. Heuristic Search	3: Informed search, A* and heuristics	—
3. Constraint Satisfaction and Local Search	4: Local search	CS221: Factor graphs 1, constraint satisfaction problems
4. Adversarial Search and Games	5: Games, minimax, pruning · 6: Expectimax, Monte Carlo tree search	—
5. Propositional Logic and Satisfiability	7: Propositional logic and planning · 8: Logical inference, theorem proving, DPLL	CS221: Logic 1
6. First-Order Logic and Knowledge Representation	9: First-order logic	CS221: Logic 2
7. Automated Planning	7: Propositional logic and planning	Basel slide sets (no recordings)
8. Bayesian Networks and Markov Networks	10: Intro to probability · 11: Bayes nets	10-708 lecture 2: Undirected models · lecture 3: Directed models · CS221: Factor graphs 2
9. Exact Inference	12: Bayes nets, inference	10-708 lecture 4: Exact inference
10. Approximate Inference	13: Bayes nets, sampling	10-708 lecture 7: Variational inference 1 · 8: Variational inference 2 · 9: Sampling 1 · 10: Sampling 2
11. Temporal Probabilistic Models	14: Markov chains, HMMs · 15: Forward and Viterbi, particle filtering	10-708 lecture 6: HMM and CRF
12. Decision Theory and the Value of Information	16: Utility theory, decision networks, VPI	—
13. Causal Inference	—	Brady Neal: 1 · 2 · 3 · 4 · 5 · 6 · 8 · 10 · 13 · 10-708 lecture 17 · lecture 18
14. Learning Graphical Models (optional)	—	10-708 lecture 5: Parameter estimation · lecture 16: Structure learning
15. Game Theory and Multiagent Systems (optional)	—	CS364A: 2 · 7 · 11 · 13 · 17 · 18 · 20