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 (廖偉翔, 廖尉琳, 廖勇冠, 王俊煒, 郭俊麟, 郭哲宇, 呂昱穎, 郭子聖) are available at PM 19:30-20:30 in ED 912 in week days.
Midterm Exam (30%), Final Exam (40%), Homework (30%), Class Attendance (10%)
-
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 |
| 第 2 週 | Curve Fitting & Model Selection |
| 第 3 週 | Decision Theory & Information Theory |
| 第 4 週 | Probability Functions - Binomial, Multinomial, Beta, Dirichlet, Gaussian & Student t Distributions |
| 第 5 週 | Generative Models - Least Squares & Regularized Least Squares, Maximum Likelihood, Maximum a Posteriori |
| 第 6 週 | Bayesian Linear Regression, Bayesian Model Comparison & The Evidence Framework |
| 第 7 週 | Discriminant Function - Least Squares, Fisher's Discriminant & Perceptron Algorithm |
| 第 8 週 | Discriminative Model - Logistic Regression, Laplace Approximation & Bayesian Logistic Regression |
| 第 9 週 | Midterm Exam & Homework |
| 第 10 週 | Kernel Methods |
| 第 11 週 | Gaussian Process |
| 第 12 週 | Sparse Kernel Methods |
| 第 13 週 | Support Vector Machine |
| 第 15 週 | Mixture Models and EM |
| 第 15 週 | Relevance Vector Machine |
| 第 16 週 | Hidden Markov Models |
| 第 17 週 | Approximate Inference |
| 第 18 週 | Final Exam & Homework |
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.
- 地點
- ED912
- 時間
- PM17:00-PM18:00 on Monday
- 聯絡方式
- jtchien@nctu.edu.tw