CMPE58K - Bayesian Statistics and Machine Learning
Fall 2011
Instructor: Ali Taylan Cemgil
(Volunteer) TA's: Arman Boyaci, Serhan Danis
Bogazici University, Department of Computer Engineering, Istanbul, Turkey
Announcements
(28.10) Assignment 3 is posted, See assignments tab,
(07.10) Second assignment is posted, See assignments tab,
(30.09) First assignment is posted fall2011-assignment1.pdf Due 5 Oct, 9:00
(30.09) There will be a PS on 5 Oct, 9:00-10:00 ETA A6
(26.09) The lectures will be in ETA A6 (Cmpe building), Wed 234 (10:00-13:00). Already 25 are registered so it won't be possible to change the time.
(26.09) Every week after the second, there will be a problem session before the lecture (9:00-10:00) where assignments will be discussed in detail.
Slides
Lecture00 Course Structure
Lecture01 Introduction, Probability tables,
Lecture02 Graphical Models, Independence, Conditional Independence, Additional slides from Bishop
Lecture03 Bayesian Learning, Applications
Lecture04 Exponential Family, Unvariate Probability Distributions, Conjugate Priors
Lecture05 Multivariate Distributions: Gaussian, Multivariate Bernoulli
Lecture06 Sequential Data, Markov models, Hidden Markov Models (HMM's), Inference, Forward Backward algorithm
Lecture07 Approximate inference, Variational Methods: Variational Bayes, ICM, Expectation Maximisation (EM), Variational Methods, AR Model Example
Lecture08 Correction Smoother, Junction tree algorithm
Lecture09 Linear Dynamical Systems (LDS's), Inference in LDS, Kalman Filter and Smoother
Supplement Changepoint models
Timetable
Sep 28, Wed Introduction, Bayes Theorem, clustering, k-means, generative models
Oct 05, Wed Derivation of the k-means algorithm from the generative model as ICM, Multinomial density, Indicator representations
Oct 12, Wed Probability tables, Graphical models, Independence, Conditional Independence
Oct 19, Wed Midterm I (ATC absent - WASPAA)
Oct 26, Wed Hidden Markov Models, Filtering, smoothing, Forward pass, Viterbi path
Nov 02, Wed
Nov 09, Wed Kurban Bayrami
Nov 16, Wed
Nov 23, Wed
Nov 30, Wed
Dec 07, Wed
Dec 14, Wed Midterm II (ATC absent - NIPS)
Dec 21, Wed
Dec 28, Wed
$\Large p(\lambda| x) = p(x|\lambda) p(\lambda) / \int d\lambda' p(x|\lambda') p(\lambda') $
|