生物機器學習
Machine Learning in Computational Biology
| 節 | 週二 |
|---|---|
7 15:30–16:20 | 生物機器學習 BI301（博愛） 3 節連堂 |
8 16:30–17:20 | |
9 17:30–18:20 |
* 根據陽明交大上課時間表所列
In this course, students will learning and explore the intersection of molecular biology and machine learning, uncovering the tremendous potential for solving complex biological problems using state-of-the-art computational techniques. The goal of this course is to provide you with a comprehensive understanding of how machine learning can be applied to biological data, enabling breakthroughs in fields such as genomics, proteomics, drug discovery, and biomedical research. A wide range of topics are covered in this course, including: 1. Introduction to Machine Learning: We will start with the fundamentals of machine learning, covering supervised and unsupervised learning techniques, model evaluation, and data preprocessing. 2. Biological Data Types: Understanding the unique characteristics of biological data is essential for effective analysis. We will explore diverse data types, including genomic sequences, gene expression data, protein structures, and biological networks. 3. Feature Extraction and Dimensionality Reduction: With the high dimensionality of biological data, feature extraction and dimensionality reduction techniques play a vital role in reducing complexity and extracting meaningful information. We will study various methods such as principal component analysis (PCA), t-SNE, and feature selection algorithms. 4. Predictive Modeling in Genomics: Genomic data holds enormous potential for personalized medicine and understanding disease mechanisms. We will explore how machine learning algorithms can be used to predict gene functions, identify disease-associated genetic variants, and construct gene regulatory networks. 5. Drug Discovery and Pharmacogenomics: Machine learning has the potential to accelerate drug discovery processes and facilitate precision medicine. We will investigate how computational models can aid in virtual screening, drug target identification, and predicting drug response based on genomic information. 6. Deep Learning in Bioinformatics: Deep learning architectures, such as convolutional neural networks (CNNs) and recurrent neural networks (RNNs), have shown remarkable performance in various biological applications. We will examine how these models can be applied to tasks like image analysis, protein structure prediction, and genomics. Throughout the course, you will have the opportunity to work on hands-on projects and gain practical experience in applying machine learning techniques to real biological datasets. By the end, you will have a solid foundation in biological machine learning and be equipped to contribute to this rapidly evolving field.
Programming / Bioinformatics / Computational Biology
無備註
Lectures, Discussion and Hands-on Practice
Assignments (40%) + Presentation(20%) + Final Project Report (40%)
教師未提供此項資料
| 週次 | 主題 |
|---|---|
| 第 1 週 | Course Introduction and Biological Data 2025-09-02(二) 時數：[2025-09-02]李宗夷(3.00) |
| 第 2 週 | Data Preparation and Preprocessing 2025-09-09(二) 時數：[2025-09-09]李宗夷(3.00) |
| 第 3 週 | Features Encoding and Investigation - One Hot Encoding, Amino Acid Composition, Amino Acid Pair Composition, Positional Weight Matrix, CKSAAP, PSSM, Sequence Motifs 2025-09-16(二) 時數：[2025-09-16]李宗夷(3.00) |
| 第 4 週 | Model Construction and Performance Evaluation 2025-09-23(二) 時數：[2025-09-23]李宗夷(3.00) |
| 第 5 週 | Supervised Machine Learning Methods I - Profile Hidden Markov Models 2025-09-30(二) 時數：[2025-09-30]李宗夷(3.00) |
| 第 6 週 | Supervised Machine Learning Methods II - Decision Tree, Random Forest, Basian Network 2025-10-07(二) 時數：[2025-10-07]李宗夷(3.00) |
| 第 7 週 | Self study (NYCU-UCSD Bilateral Symposium) 2025-10-14(二) 時數：[2025-10-14]李宗夷(3.00) |
| 第 8 週 | Supervised Machine Learning Methods III - Linear Regression, Support Vector Machine, Neural Networks 2025-10-21(二) 時數：[2025-10-21]李宗夷(3.00) |
| 第 9 週 | Unsupervised Machine Learning Methods I - Hierarchical and non-Hierarchical Clustering methods 2025-10-28(二) 時數：[2025-10-28]李宗夷(3.00) |
| 第 10 週 | Unsupervised Machine Learning Methods II - Association Rule and Maximal Dependence Decomposition 2025-11-04(二) 時數：[2025-11-04]李宗夷(3.00) |
| 第 11 週 | Unsupervised Machine Learning Methods III - Dimensionality Reduction and Principal Component Analysis (PCA) 2025-11-11(二) 時數：[2025-11-11]李宗夷(3.00) |
| 第 12 週 | Deep Learning I - Convolutional Neural Networks (CNNs), Self Attention and Transformer 2025-11-18(二) 時數：[2025-11-18]李宗夷(3.00) |
| 第 13 週 | Deep Learning II - Generative Adversarial Networks (GANs), Variational Autoencoders (VAEs) and Large Language Models (LLMs) 2025-11-25(二) 時數：[2025-11-25]李宗夷(3.00) |
| 第 14 週 | Final Project - Oral Presentation 2025-12-02(二) 時數：[2025-12-02]李宗夷(3.00) |
| 第 15 週 | Final Project - Oral Presentation 2025-12-09(二) 時數：[2025-12-09]李宗夷(3.00) |
| 第 16 週 | Final Competition - Independent Testing 2025-12-16(二) 時數：[2025-12-16]李宗夷(3.00) |
1. Ethem Alpaydin,“Introduction to Machine Learning", The MIT Press, 3rd edition, 2014 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. Kai Hwang and Min Chen,“Big-Data Analytics for Cloud, IoT and Cognitive Computing”, WILEY, First edition.
- 地點
- Room 317, BioICT Building, Poai Campus
- 時間
- Wednesdays 14:00 - 17:30
- 聯絡方式
- leetzongyi@nycu.edu.tw / 03-5712121 #56947