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 will be provided. Teacher assistants (黃伯鈞、劉品彥、游翔竣、葉家愷、陳柏全) are available at PM 19:00-20:00 in ED 708 in week days. Any questions about ML and homework are welcome. Appointments are required.
Temporary Grading Policy: Final Exam (40%), Homework (30%), Final Project (30%)
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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 2023-09-15(五) |
| 第 2 週 | 停課一周 2023-09-22(五) |
| 第 3 週 | 中秋節 2023-09-29(五) |
| 第 4 週 | Model Selection, Decision Theory, Information Theory 2023-10-06(五) |
| 第 5 週 | Probability Functions - Binomial, Multinomial, Beta, Dirichlet, Gaussian, Student t Distributions (1st Homework) 2023-10-13(五) |
| 第 6 週 | Generative Models - Least Squares, Regularized Least Squares, Maximum Likelihood, Maximum a Posteriori 2023-10-20(五) |
| 第 7 週 | 停課一周 2023-10-27(五) |
| 第 8 週 | Bayesian Linear Regression, Bayesian Model Comparison, The Evidence Framework 2023-11-03(五) |
| 第 9 週 | Discriminant Function - Least Squares, Fisher's Discriminant, Perceptron Algorithm (Proposal) 2023-11-10(五) |
| 第 10 週 | Discriminative Model - Logistic Regression, Laplace Approximation, Bayesian Logistic Regression 2023-11-17(五) |
| 第 11 週 | Kernel Methods, Gaussian Process 2023-11-24(五) |
| 第 12 週 | Sparse Kernel Methods - Large Margin Classifier 2023-12-01(五) |
| 第 13 週 | Support Vector Machine, Relevance Vector Machine (2nd Homework) 2023-12-08(五) |
| 第 14 週 | Mixture Models and EM 2023-12-15(五) |
| 第 15 週 | Hidden Markov Models, Approximate Inference 2023-12-22(五) |
| 第 16 週 | Final Exam 2023-12-29(五) |
| 第 17 週 | Project Presentation 2024-01-05(五) |
| 第 18 週 | Project Presentation 2024-01-12(五) |
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
- ED708
- 時間
- PM17:30-PM18:30 on Monday (with appointment)
- 聯絡方式
- jtchien@nycu.edu.tw