CMPE547 - Bayesian Statistics and Machine Learning
Fall 2018
Instructor: Ali Taylan Cemgil
(Volunteer) TA: Onur Poyraz
Bogazici University,
Department of Computer Engineering,
Istanbul, Turkey
Announcements
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)
Slides
Exercise Booklet
Timetable
| 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.
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