機器學習(英文授課)
Machine Learning
| 節 | 週一 | 週四 |
|---|---|---|
3 10:10–11:00 | 機器學習(英文授課) EC115 2 節連堂 | |
4 11:10–12:00 | ||
7 15:30–16:20 | 機器學習(英文授課) EC115 |
* 根據陽明交大上課時間表所列
(1) To build big picture on machine learning field and equip with the ability of implementation machine learning techniques. This course will introduce the theory behind the techniques, so a great deal of time will spend on the mathematics foundation. (2) To understand the properties of different learning algorithms and learn how to use, when to use, which to use, under different scenarios.
Calculus, Probability, Statistics, Linear Algebra, Introduction of Machine learning or equivelent
Involvement (10%), homework (30%) mid term (30%), final (30%)
- Probability and information theory
- Regression and classification
- Dimension reduction and feature extraction
- Distribution and Statistics
- Kernel methods
- Generative Models - Clustering
- Generative Models - Dimensionality Reduction
- Generative Models - Graphical Models
| 週次 | 主題 |
|---|---|
| 第 1 週 | 1. My teaching method, overview of the course 2. Basics of probability (Joint, conditional probability and independence) 3. Basics of information theory (Entropy, relative entropy, mutual information) 4. Bayes theorem (Maximum likelihood, conditional independence, naive Bayes classifiers, Bayesian network) |
| 第 3 週 | 1. Classification and Regression 2. Linear regression (MLE) 3. Logistic regression 4. Regularization (MAP, Ridge and Lasso) 5 .Basics of optimization |
| 第 5 週 | 1. Correlation Coefficient 2. PCA 3. NMF 4. FFT |
| 第 7 週 | 1. Distribution (Beta, Gaussian, etc.) 2. Moment Generation Function 3. Special function 4. Conjugate prior |
| 第 9 週 | Midterm |
| 第 10 週 | 1. Kernel Method 2. Gaussian Process 3. Support Vector Machine |
| 第 12 週 | 1. K-Means, Kernel K-Means 2. Spectral Clustering 3. DBSCAN 4. Hierarchical Clustering |
| 第 14 週 | 1. PCA, Kernel PCA 2. LDA 3. IsoMap 4. LLE 5. Laplacian Eigenmap 6. t-SNE |
| 第 16 週 | 1. Directed Graph (Bayesian Networks) 2. Undirected Graph (Markov Random Fields) 3. Factor Graph / Belief Propagation 4. HMM 5. Sampling |
| 第 18 週 | 1. Sampling 2. Final exam |
[1] Christopher Bishop, Pattern Recognition and Machine Learning, Springer, 2007 [2] A. Smola and S.V.N. Vishwanathan, Introduction to Machine Learning, Cambridge University Press, Oct. 2010
- 地點
- office
- 時間
- TBA
- 聯絡方式