CE5053 - Statistical Learning (Fall 2024)

Meeting time

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Recommended Textbook

Other recommended readings

Staff

Grading

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Progress (tentative)

Week Date Content Exercise
1 9/9 Overview of the course
2 9/16 1. Statistical learning
2. Important probability and statistics concepts
3 9/23 Bayesian network and d-separation Exercise 1 (due: 10/6 23:59:59)
4 9/30 1. Naive Bayesian
2. Bayesian network modeling examples
5 10/7 1. Markov Random Fields
2. Point estimation: MLE and MAP
Exercise 2 (due: 10/20 23:59:59)
6 10/14 Exponential family and conjugate prior
7 10/21 1. Hypothesis testing
2. Midterm review
8 10/28 Midterm exam
9 11/4 EM algorithm
10 11/11 Invited talk (長庚醫院鄭啟桐醫師) Exercise 3 (due: 11/24 23:59:59)
11 11/18 Invited talk (陽明交大曾意儒教授)
12 11/25 Invited talk (KKCompany 官順暉、羅經凱)
1. Monte Carlo methods
2. Variational inference
13 12/2 1. Monte Carlo methods
2. Inverse transform sampling, reject sampling, and importance sampling
3. Markov chain
Exercise 4 (due: 12/15 23:59:59)
14 12/9 1. Markov Chain Monte Carlo: Metropolis-Hastings and Gibbs sampling
2. Latent Dirichlet Allocation with MCMC
3. Bayesian linear regression: closed-form vs MCMC vs VI
15 12/16 1. Gaussian process
2. Summary of the course
16 12/23 Final exam
17 12/30 Flexible learning week: Bayesian neural network
18 1/6 Flexible learning week: Interpretable machine learning