Tentative Schedule

Date	Content	Reading	Lecture slides
	Probabilistic graphical models	Optional reading: Lauritzen Ch3, KollerFriedman Ch3-4,
9/27	Welcome/overview	probability.pdf and statistics.pdf (my notes on background materials to help with hw0)	Live lecture note1
10/2	Directed graphical models	graph.pdf markov.pdf	Proof of equivalence of Markov properties prelecture_note2.pdf
10/4	Undirected graphical models		Pre-lecture note 3 Lecture note 3
10/9	Relations between graphical models		prelec4 livelecture_note4.pdf
10/11	Continuation of relations between GMs		prelec5
	Belief propagation	Optional reading: KollerFriedman Ch9,10,13,7, Lauritzen Ch5
10/16	Sum-product algorithm (belieif propagation) and max-product algorithm	bp.pdf maxproduct.pdf	Prelecture 6 Lecture 6
10/18	Factor Graphs		Prelecture 7 Lecture 7 notes
10/23	Low-density parity codes, Junction Trees, Density evolution	densityevolution.pdf	Prelecture 8 Notes Lecture 8 Notes
10/25	Density evolution		prelecture_note9.pdf livelecture_note9.pdf
10/30	Gaussian graphical models	gauss.pdf	Prelec10.pdf Lec10.pdf
11/1	Gaussian Belief Propagation		prelecture_note11.pdf livelecture_note11.pdf Goodnotes file Audio recording
	Variational metohds and sampling	Optional reading: KollerFriedman Ch11-12,
11/6	Variational methods	variational.pdf	Prelecture 12 Lecture 12
11/8	Variational methods		prelecture_note13.pdf livelecture_note13.pdf
11/13	Variational methods
11/15	Markov chain Monte Carlo	mcmc.pdf	sampling.pdf
11/20	Markov chain Monte Carlo		sampling_1120.pdf
	Learning graphical models	Optional reaeding: KollerFridman Ch16-20,
11/22	Learning graphical models	learning.pdf	prelecture_note17.pdf livelecture_note17.pdf
11/27	Learning graphical models		prelecture_note18.pdf livelecture_note18.pdf
11/29	Causal structure discovery		livelecture_note19.pdf
12/4	Causal structure discovery / Summary