機器學習
Machine Learning
| 節 | 週五 |
|---|---|
5 13:20–14:10 | 機器學習 ED219 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
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%)
| 週次 | 主題 |
|---|---|
| 第 1 週 | Introduction to Machine Learning, Curve Fitting |
| 第 2 週 | 停課一周 |
| 第 3 週 | 中秋節 |
| 第 4 週 | Model Selection, Decision Theory, Information Theory |
| 第 5 週 | Probability Functions - Binomial, Multinomial, Beta, Dirichlet, Gaussian, Student t Distributions (1st Homework) |
| 第 6 週 | Generative Models - Least Squares, Regularized Least Squares, Maximum Likelihood, Maximum a Posteriori |
| 第 7 週 | 停課一周 |
| 第 8 週 | Bayesian Linear Regression, Bayesian Model Comparison, The Evidence Framework |
| 第 9 週 | Discriminant Function - Least Squares, Fisher's Discriminant, Perceptron Algorithm (Proposal) |
| 第 10 週 | Discriminative Model - Logistic Regression, Laplace Approximation, Bayesian Logistic Regression |
| 第 11 週 | Kernel Methods, Gaussian Process |
| 第 12 週 | Sparse Kernel Methods - Large Margin Classifier |
| 第 13 週 | Support Vector Machine, Relevance Vector Machine (2nd Homework) |
| 第 14 週 | Mixture Models and EM |
| 第 15 週 | Hidden Markov Models, Approximate Inference |
| 第 16 週 | Final Exam |
| 第 17 週 | Project Presentation |
| 第 18 週 | Project Presentation |
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