CE5053 - Statistical Learning (Fall 2020)

"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: Hung-Hsuan Chen (陳弘軒)
Office hour: after class or by appointment.
TA: 張欣茹、曾莊
Office hour: by appointment

Week	Date	Content	Exercise
1	9/14	Overview of the course
2	9/21	1. Statistical learning 2. Important probability and statistics concepts
3	9/28	Bayesian network and d-separation Invited talk (Dr. Wen-Yu Hua)
4	10/5	1. Bayesian network and d-separation 2. Naive Bayesian	Exercise 1 (due: 10/18 23:59:59)
5	10/12	1. Naive Bayesian 2. Modeling examples TA session: introduction to Python and popular scientific libraries in Python
6	10/19	1. Markov Random Fields 2. Exact inference by variable elimination
7	10/26	Exact inference by message passing	Exercise 2 (due: 11/9 14:00:00)
8	11/2	1. Midterm review 2. Hypothesis testing
9	11/9	Midterm exam
10	11/16	1. MLE 2. MAP
11	11/23	Exponential family and conjugate prior for Bayesian estimation and prediction
12	11/30	1. EM algorithm 2. Monte Carlo sampling
13	12/7	1. Monte Carlo sampling 2. Markov chain, Gibbs Sampling, and Metropolis-Hastings 3. Markov Chain Monte Carlo (MCMC)	Exercise 3 (due: 12/21 14:00)
14	12/14	1.Markov Chain Monte Carlo Invited talk (Prof. Tseng-Yi Chen)
15	12/21	Bayesian linear regression	Exercise 4 (due: 1/3 23:59:59)
16	12/28	Gaussian process
17	1/4	1. Hyperparameter tuning by Bayesian optimization 2. Final exam review
18	1/11	Final exam