機器學習
Machine Learning
| 節 | 週五 |
|---|---|
5 13:20–14:10 | 機器學習 ED219（光復） 3 節連堂 |
6 14:20–15:10 | |
7 15:30–16:20 |
* 根據陽明交大上課時間表所列
Machine learning, a branch of artificial intelligence, is a scientific discipline concerned with the design and development of algorithms that allow computers to evolve behaviors based on empirical data from sensor data or databases. A major focus is to automatically learn to recognize complex patterns and make intelligent decisions based on data. This course shall deliver fundamental theories of machine learning which can be applied for many intelligent information systems.
Calculus, Linear Algebra, Probability & Statistics
無備註
Lecture notes and slides will be provided. Teacher assistants (朱長亭, 張哲瑋, 田修瑋, 王意麟, 黃毓涵, 張譽瀚, 李尚恩) are available at PM 19:00-20:00 in ED 912 in week days. Any questions about ML and homework are welcome.
Temporary Grading Policy: Midterm Exam (28%), Final Exam (37%), Homework (35%)
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1. Introduction 2. Probability Distributions 3. Linear Models for Regression 4. Linear Models for Classification 5. Kernel Methods 6. Sparse Kernel Methods 7. Mixture Models and EM 8. Approximate Inference
| 週次 | 主題 |
|---|---|
| 第 1 週 | Introduction to Machine Learning - Curve Fitting Sep 18 |
| 第 2 週 | Model Selection, Decision Theory & Information Theory Sep 25 |
| 第 3 週 | National Holiday Oct 2 |
| 第 4 週 | National Holiday Oct 9 |
| 第 5 週 | Probability Functions - Binomial, Multinomial, Beta, Dirichlet, Gaussian & Student t Distributions Oct 16 (Homework 1) |
| 第 6 週 | Generative Models - Least Squares & Regularized Least Squares, Maximum Likelihood, Maximum a Posteriori Oct 23 |
| 第 7 週 | Bayesian Linear Regression, Bayesian Model Comparison & The Evidence Framework Oct 30 |
| 第 8 週 | Discriminant Function - Least Squares, Fisher's Discriminant & Perceptron Algorithm Nov 6 |
| 第 9 週 | Midterm Exam Nov 13 |
| 第 10 週 | Discriminative Model - Logistic Regression, Laplace Approximation & Bayesian Logistic Regression Nov 20 (Homework 2) |
| 第 11 週 | Kernel Methods & Gaussian Process Nov 27 |
| 第 12 週 | Sparse Kernel Methods - Large Margin Classifier Dec 4 |
| 第 13 週 | Support Vector Machine & Relevance Vector Machine Dec 11 |
| 第 15 週 | Mixture Models and EM Dec 18 (Homework 3) |
| 第 15 週 | Hidden Markov Models & Approximate Inference Dec 25 |
| 第 16 週 | National Holiday Jan 1 |
| 第 17 週 | Final Exam Jan 8 |
| 第 18 週 | Supplement Teaching Jan 15 |
1. C. M. Bishop, Pattern Recognition and Machine Learning, Springer, 2006. 2. S. Watanabe and J.-T. Chien, Bayesian Speech and Language Processing, Cambridge University Press, 2015. 3. J.-T. Chien, Source Separation and Machine Learning, Academic Press, 2018. 4. M.-W. Mak and J.-T. Chien, Machine Learning for Speaker Recognition, Cambridge University Press, 2020.
- 地點
- ED912
- 時間
- PM17:30-PM18:30 on Monday (with appointment)
- 聯絡方式
- jtchien@nctu.edu.tw