STAT891 | Probabilistic Graphical Models

Spring 2019

Time: Mon/Thu 8-9:30am at SCB4401

Instructor

References

Further readings


DateTopicReferencesNotes/HW
Dec 19 IntroductionKoller ch.2Lecture 1
Dec 23 Probability review, Bayesian networksKoller ch.3Lecture 2
Dec 27 Bayesian networks (cont.)Koller ch.3Lecture 3
Jan 6 Parameter estimationKoller ch.17.1-17.2Lecture 4
Jan 10 Gaussian BN, Bayesian statisticsKoller ch.17.2-17.3Lecture 5, Homework 1
Jan 13 Bayesian parameter estimationKoller ch.17.4Lecture 6
Jan 17 Constraint-based learningKoller ch.18.1-18.2Lecture 7, Homework 2
Jan 20 Score-based learningKoller ch.18.3Lecture 8
Feb 3 Bayesian score, Structure searchKoller ch.18.3-18.4Lecture 9
Feb 4 Structure search, Missing dataKoller ch.18.4, 19.1Lecture 10
Feb 7 MAR, Identifiability Koller ch.19.1Lecture 11
Feb 10 Gradient ascent, EM algorithmKoller ch.19.2Lecture 12, Homework 3
Feb 14 Basic information theoryBishop ch.1.6Lecture 13
Feb 24 Latent variable models, GMM, Variable eliminationBishop ch.1.6, Koller ch.9.2-9.3Lecture 14
Feb 25 Markov random fieldsKoller ch.4.1, 4.3, 4.6soon™
Feb 25 MRF parameter estimation, MCMCKoller ch.20.2, 20.3, 20.5, Bishop ch.11.2Lecture 16