CMPE547 - Bayesian Statistics and Machine Learning

Fall 2018

Instructor: Ali Taylan Cemgil
(Volunteer) TA: Onur Poyraz
Bogazici University,
Department of Computer Engineering,
Istanbul, Turkey


  • Example Exam. You can bring an A4 cheat sheet with your hand writing.

  • The PS will be between 12:00-13:00 after the lecture, the starting date will be announced

Github site for project submissions

Jupyter Notebooks (in preperation)


Exercise Booklet



26-Sep Introduction, Review, Bayes Theorem, Probability tables Barber Ch1
03-Oct Bayesian Inference, Applications Barber Ch2, Ch3
10-Oct Directed Graphical Models, Conditional Independence, D-Separation, Undirected Graphical Models, Factor Graphs, Barber Ch4
17-Oct Exact inference in chains and trees, Sum-Product Algorithm, Bucket elimination Barber Ch5
24-Oct Probability Models, ML, MAP and Bayesian Learning, Exponential Family, Learning as Inference, the EM Algorithm Barber Ch8.1-3, Ch9.1-2
31-Oct Midterm 1, in class
07-Nov Multivariate Gaussians, Linear Models, Bayesian Linear Models Barber Ch8.4, Ch17.1-2, 18.1
14-Nov Gaussian Processes, Factor Analysis Barber Ch19.1-3, Ch21.1-4
21-Nov Discrete state space Markov models, Hidden Markov Models, Forward Backward algorithm, Filtering, Smoothing, Correction smoother Barber Ch23.1-2
28-Nov Linear Dynamical Systems (LDS's), Inference in LDS, Kalman Filter and Smoother, Dynamic Bayes nets, Switching state space models Barber Ch24.1-4 Ch25.1-2
05-Dec Midterm 2, in class
12-Dec Approximate inference, Variational Methods, Deadline for project proposal Barber Ch28.1-4
19-Dec Hierarchical models, Variational Bayes

Chapter numbers are given according to the DRAFT June 18, 2013 version of Bayesian Reasoning and Machine Learning by David Barber.

After Final exam week, there will be a half day poster Presentations for groups of three.

$\Large p(\lambda| x) = p(x|\lambda) p(\lambda) / \int d\lambda' p(x|\lambda') p(\lambda') $