CMPE547 (Past CMPE58K)  Bayesian Statistics and Machine Learning
Spring 2017
Instructor: Ali Taylan Cemgil
(Volunteer) TA: Semih Akbayrak
Bogazici University, Department of Computer Engineering, Istanbul, Turkey
Announcements
(28.03) Example Exam. You can bring an A4 cheat sheet with your hand writing.
(08.02) The PS will be between 9:0010:00 before the lecture, the starting date will be announced
Github site for project submissions
Jupyter Notebooks (in preperation)
Slides
Exercise Booklet
booklet.pdf
Timetable
08Feb  Introduction, Review, Bayes Theorem, Probability tables  Barber Ch1 
15Feb  Bayesian Inference, Applications  Barber Ch2, Ch3 
22Feb  Directed Graphical Models, Conditional Independence, DSeparation, Undirected Graphical Models, Factor Graphs,  Barber Ch4 
01Mar  Exact inference in chains and trees, SumProduct Algorithm, Bucket elimination  Barber Ch5 
08Mar  Probability Models, ML, MAP and Bayesian Learning, Exponential Family, Learning as Inference, the EM Algorithm  Barber Ch8.13, Ch9.12 
15Mar  Multivariate Gaussians, Linear Models, Bayesian Linear Models  Barber Ch8.4, Ch17.12, 18.1 
22Mar  Gaussian Processes, Factor Analysis  Barber Ch19.13, Ch21.14 
29Mar  Midterm, in class  
05Apr  Discrete state space Markov models, Hidden Markov Models, Forward Backward algorithm, Filtering, Smoothing, Correction smoother  Barber Ch23.12 
12Apr  Linear Dynamical Systems (LDS's), Inference in LDS, Kalman Filter and Smoother, Dynamic Bayes nets, Switching state space models  Barber Ch24.14 Ch25.12 
19Apr  Approximate inference, Variational Methods, Deadline for project proposal  
26Apr  Spring Break  
03May  Hierarchical models, Variational Bayes  Barber Ch28.14 
10May  Review  

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