AI in biology and medicine
AI in biology and medicine
Lecturer: Kuan-Yuan Chang (張光遠 )
Email: kchang@email.ntou.edu.tw
Phone: (02)24622192 #6649
Webpage: sites.google.com/view/kchang
Course ID: M57013R6
Credits: 3
Objective:
Artificial intelligence (AI) has advanced modern biology and medicine. The goal of this course is to teach computational scientists and biomedical scientists the AI methods used in modern biomedicine. This course primarily focuses on the fundamentals of machine learning methods, which can be applied to biological and medical data.
Course Prerequisites: Linear algebra and Programming experiences are required
Outline:
1. Introduction to artificial intelligence and biomedical applications
2. Model performance measurement
3. Supervised learning: regression analysis, logistic regression
4. Supervised learning: recurrent neural networks
5. Supervised learning: transformers
6. Unsupervised learning: K-means, expectation-maximization
7. Unsupervised learning: generative models
8. Reinforcement learning
9. Genomics and biomedical databases
10. Genomics and biomedical DB: next-generation sequencing
11. Applications of deep neural networks in biomedicine
12. Language-based artificial intelligence methods: word embeddings
13. Biomedical data visualization: t-SNE
14. Biomedical image processing: convolutional neural networks
15. Artificial intelligence assisted medical decision making
16. Final report
Teaching Method:
Class Notes
Journal articles
Homework
Reference:
B. Alberts, A. Johnson, J. Lewis, D. Morgan, and M. Raff Molecular Biology of the Cell. W. W. Norton & Company, 2014.
C.M. Bishop Pattern Recognition and Machine Learning. Springer, 2011.
D.S. Sivia and J. Skilling Data Analysis: A Bayesian Tutorial. Oxford, 2006.
Course Schedule (subject to change):
Adjust according to the students' learning situation.
Evaluation:
Programming Assignments: 40%
Presentations + Class Participation: 20%
Midterm: 20%
Final: 20%