CE5053 - Statistical Learning (Fall 2018)

"An Introduction to Probabilistic Graphical Models" by Michael I. Jordan
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"Probabilistic Graphical Models: Principles and Techniques" by Daphne Koller and Nir Friedman
"Machine Learning: a Probabilistic Perspective" by Kevin Murphy
"Pattern Recognition and Machine Learning" by Christopher Bishop

Instructor: 陳弘軒 Hung-Hsuan Chen
Office hour: after class or by appointment.
TA: 蘇俊儒、陳廷睿
Office hour: TBA

Week	Date	Content	Exercise
1	9/10	Overview of the course
2	9/17	1. Probabilistic learning 2. Probability (joint distribution, marginal distribution, conditional distribution) 3. Factorization 4. Bayesian network	Exercise 1 (due: 10/14 23:59:59)
3	9/24	Holiday
4	10/1	1. Bayesian network 2. D-separation 3. TA session: Introduction to Python and popular scientific libraries in Python
5	10/8	1. Naive Bayesian 2. Markov random field
6	10/15	1. Markov random field 2. Variable elimination
7	10/22	1. Variable elimination 2. Message passing 3. Clique trees
8	10/29	1. Message passing 2. Midterm review	Exercise 2 (due: 11/11 23:59:59)
9	11/5	Midterm exam
10	11/12	Learning in Fully Observed Bayesian Networks and exponential family
11	11/19	The First Symposium on Artificial Intelligence Interactive Systems
12	11/26	Exponential family and conjugate prior	Exercise 3 (due: 12/9 23:59:59)
13	12/3	Learning in Partially Observed Network by EM
14	12/10	Markov Chain Monte Carlo (MCMC) and Latent Dirichlet Allocation (LDA)
15	12/17	Variational inference	Exercise 4 (due: 12/30 23:59:59)
16	12/24	1. Variational inference on LDA 2. Final wrap-up
17	12/31	Holiday
18	1/7	Final exam