生物機器學習
Machine Learning in Computational Biology
| 節 | 週二 |
|---|---|
2 09:00–09:50 | 生物機器學習 EF252 3 節連堂 |
3 10:10–11:00 | |
4 11:10–12:00 |
* 根據陽明交大上課時間表所列
This course is devoted to introducing and developing some effective and efficient machine learning techniques for analyzing several important computational biology problems, such as bioinformatics and bioimage informatics. At the same time, novel machine learning methods are presented and analyzed. The analysis should help shed some light on this new and exciting area, and should be especially useful to professionals in Bioinformatics and Machine Learning fields. The major topics are as follows: Introduction to machine learning .Computational Methodologies .Optimization Concept .k-Nearest neighbor .Decision trees .Artificial neural networks .Support vector machines (SVM) Roles of machine learning in: .Artificial intelligence .Image processing .Pattern recognition .Simulation and modeling Tool: .Waikato Environment for Knowledge Analysis (WEKA) .LibSVM Applications: . Life and medical sciences . Gene network . Protein-DNA binding . Microarray data analysis . Molecular Bioimaging
課堂講授、操作、討論及報告
課堂參與20%,課堂作業30%,期末口頭與書面報告50%
- 簡介
- 最佳化演算法
- 機器學習演算法
- 深度學習演算法
- 生物機器學習實例應用
- 學習評量
- 機器學習工具
- 課程作業
| 週次 | 主題 |
|---|---|
| 第 1 週 | 機器學習基本概念與應用 (Basic Concept and Applications of Machine Learning) |
| 第 2 週 | 機器學習概念 (Concept of Machine Learning) |
| 第 3 週 | 機器學習概念 (Concept of Machine Learning) WEKA的使用指南 (Tutorial of Weka) |
| 第 4 週 | 機器學習演算法概要 (Concept of Machine Learning Algorithm) |
| 第 5 週 | 決策樹 (Decision Tree) |
| 第 6 週 | 貝氏分類器 (Bayes classifier) 最近鄰居分類器 (Nearest-Neighbor Classification) |
| 第 7 週 | 支持向量機 (Support Vector Machine) LibSVM的使用指南 (Tutorial of LibSVM) |
| 第 8 週 | 機器學習分類案例探討 (Cases Study in Machine Learning Classification) |
| 第 9 週 | 最佳化相關演算法 (Optimization Related Algorithm) 分群 (Clustering) |
| 第 10 週 | 回歸 (Regression) 正規化 (Regularization) |
| 第 11 週 | 降維 (Dimensionality Reduction) |
| 第 12 週 | 集成學習 (Ensemble Learning) |
| 第 13 週 | 機器學習回歸案例探討 (Cases Study in Machine Learning Regression) |
| 第 14 週 | 類神經網路 (Neural Networks) |
| 第 15 週 | 深度學習 (Deep Learning) |
| 第 16 週 | 期末口頭報告:期末專題成果(一) |
| 第 17 週 | 開國紀念日放假 |
| 第 18 週 | 期末口頭報告:期末專題成果(二) |
1. Haifeng Li, Applications of Machine Learning Techniques to Bioinformatics, VDM Verlag, ISBN 3639054407, 2008. 2. S. Mitra, S. Datta, T. Perkins and G. Michailidis, Introduction to Machine Learning and Bioinformatics, New York: Chapman & Hall/CRC Press, ISBN 978-1584886822, 2008. 3. Analysis of biological data, edited by S. Bandyopadhyay, U. Maulik and J. T. L. Wang, World Scientific, 2007.