生物機器學習
Machine Learning in Computational Biology
| 節 | 週三 |
|---|---|
5 13:20–14:10 | 生物機器學習 BI310 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
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 .Naive Bayes classifier . Artificial neural networks .Support vector machines . Deep learning and transformer, etc. Roles of machine learning in: .Artificial intelligence .Image processing .Pattern recognition .Simulation and modeling Tool: .Waikato Environment for Knowledge Analysis (WEKA) .scikit-learn .scikit-survival .Distributed Evolutionary Algorithm in Python (DEAP) Applications: . Biomedical sciences . Protein-DNA binding . Microarray and NGS data analysis . Transcriptome analysis . Molecular Bioimaging
Programming / Bioinformatics / Computational Biology
助教電子信箱:azetry.bt10@nycu.edu.tw
課堂講授、操作、討論及報告 課堂表現10%,課堂作業40%,期末專題(含期中提案、口頭報告與書面報告)50%
- 簡介
- 機器學習演算法
- 特徵選取與最佳化建模
- 深度學習演算法
- 生物機器學習實例應用
- 機器學習工具
- 專題報告與講評
| 週次 | 主題 |
|---|---|
| 第 1 週 | 機器學習基本概念 (Basic Concept of Machine Learning)、生物醫學應用(Biomedical Applications)、學期計畫(term project) |
| 第 2 週 | 和平紀念日 |
| 第 3 週 | 決策樹 (Decision Tree)貝氏分類器 (Navie Bayes classifier)最近鄰居分類器 (Nearest-Neighbor Classifier)羅吉斯迴歸 (Logistic Regression) WEKA範例 (Tutorial of Weka) |
| 第 4 週 | 支持向量機 (Support Vector Machine)Python建模工具 (scikit-Learn) |
| 第 5 週 | 集成學習 (Ensemble Learning) 隨機森林 (Random Forest) XGBoost |
| 第 6 週 | 存活分析(Survival Analysis) 降維分析 (Dimensionality Reduction and PCA) |
| 第 7 週 | 校際活動週放假 |
| 第 8 週 | 特徵選取 (Feature selection) 最佳化建模 (optimized modeling) scikit-learn 基礎建模 |
| 第 9 週 | 深度學習 (Deep Learning)卷積神經網路 (Convolutional Neural Networks) |
| 第 10 週 | 深度生成建模 (Deep Generative Modeling) Transformer |
| 第 11 週 | 期中提案報告與老師點評 (midterm proposal) |
| 第 12 週 | 生物資訊案例探討 (Bioinformatics Case Study):TCGA 資料庫與miRNA、lncRNA與蛋白質序列分析預測 |
| 第 13 週 | 臨床決策資源系統案例探討(Case study on clinical decision-making resource system):肝癌、腎臟病、AI健檢 |
| 第 14 週 | 生醫影像案例探討 (Biomedical imaging case study) |
| 第 15 週 | 期末口頭報告:期末專題成果(一) |
| 第 16 週 | 期末口頭報告:期末專題成果(二) |
| 第 17 週 | |
| 第 18 週 |
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.
- 地點
- 博愛校區賢齊館412室
- 時間
- By appointment
- 聯絡方式
- email: syho@nycu.edu.tw phone: 56905