機器學習
Machine Learning
| 節 | 週五 |
|---|---|
5 13:20–14:10 | 機器學習 EDB26（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 (楊立任, 王心玓, 葉宜萍, 徐靖憲) 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: Midterm Exam (30%), Final Exam (35%), 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 Sep 16 |
| 第 2 週 | Curve Fitting & Model Selection & Decision Theory Sep 23 |
| 第 3 週 | Information Theory & Probability Functions Sep30 |
| 第 4 週 | Probability Functions - Binomial, Multinomial, Beta, Dirichlet, Gaussian & Student t Distributions Oct 7 (Homework 1) |
| 第 5 週 | Generative Models - Least Squares & Regularized Least Squares, Maximum Likelihood, Maximum a Posteriori Oct 14 |
| 第 6 週 | Bayesian Linear Regression, Bayesian Model Comparison & The Evidence Framework Oct 21 |
| 第 7 週 | Discriminant Function - Least Squares, Fisher's Discriminant & Perceptron Algorithm Oct 28 |
| 第 8 週 | Discriminative Model - Logistic Regression, Laplace Approximation & Bayesian Logistic Regression Nov 4 (Homework 2) |
| 第 9 週 | Kernel Methods & Gaussian Process Nov 11 |
| 第 10 週 | Midterm Exam Nov 18 |
| 第 11 週 | Sparse Kernel Methods - Large Margin Classifier Nov 25 |
| 第 12 週 | Support Vector Machine & Relevance Vector Machine Dec 2 |
| 第 13 週 | Mixture Models and EM Dec 9 (Homework 3) |
| 第 14 週 | Hidden Markov Models Dec 16 |
| 第 15 週 | Approximate Inference Dec 23 |
| 第 16 週 | Final Exam Dec 30 |
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