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 12:0013:00 after the lecture, the starting date will be announced
Github site for project submissions
Jupyter Notebooks (in preperation)
Slides
Exercise Booklet
booklet.pdf
Timetable
26Sep  Introduction, Review, Bayes Theorem, Probability tables  Barber Ch1 
03Oct  Bayesian Inference, Applications  Barber Ch2, Ch3 
10Oct  Directed Graphical Models, Conditional Independence, DSeparation, Undirected Graphical Models, Factor Graphs,  Barber Ch4 
17Oct  Exact inference in chains and trees, SumProduct Algorithm, Bucket elimination  Barber Ch5 
24Oct  Probability Models, ML, MAP and Bayesian Learning, Exponential Family, Learning as Inference, the EM Algorithm  Barber Ch8.13, Ch9.12 
31Oct  Midterm 1, in class  
07Nov  Multivariate Gaussians, Linear Models, Bayesian Linear Models  Barber Ch8.4, Ch17.12, 18.1 
14Nov  Gaussian Processes, Factor Analysis  Barber Ch19.13, Ch21.14 
21Nov  Discrete state space Markov models, Hidden Markov Models, Forward Backward algorithm, Filtering, Smoothing, Correction smoother  Barber Ch23.12 
28Nov  Linear Dynamical Systems (LDS's), Inference in LDS, Kalman Filter and Smoother, Dynamic Bayes nets, Switching state space models  Barber Ch24.14 Ch25.12 
05Dec  Midterm 2, in class  
12Dec  Approximate inference, Variational Methods, Deadline for project proposal  Barber Ch28.14 
19Dec  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') $
