ECE 69500 - Machine Learning for Bioinformatics and Healthcare
Note:
Course notes and specific research papers will be used in place of a textbook. Assessments will include homework and projects.
Course Details
Lecture Hours: 3 Credits: 3
Areas of Specialization:
- Computer Engineering
Counts as:
Normally Offered:
Each Spring
Campus/Online:
On-campus and online
Catalog Description:
This course provides an overview of machine learning techniques to address emerging challenges in bioinformatics and healthcare. The course will discuss the biomedical application of supervised learning, unsupervised learning, reinforcement learning, generative/discriminative models in genomics, proteomics, medical imaging, etc. Research cases include the latest advances in AlphaFold, cancer subtype discovery.
Required Text(s):
None.
Recommended Text(s):
None.
Lecture Outline:
| Lectures | Lecture Topics |
|---|---|
| 1 | Overview of machine learning, artificial intelligence, and biomedical data science |
| 2 | Review of probability and information theory |
| 2 | Review of linear algebra |
| 5 | Supervised learning in genetics and genomics |
| 4 | Unsupervised learning in cancer subtype discovery |
| 3 | Feature selection in biomarker detection |
| 6 | Generative models in protein structure prediction, medical imaging super resolution |
| 3 | Reinforcement learning in collective cell migration |
| 3 | Computer-aided diagnosis |
| 3 | Handling missing values in biological and medical domains |
| 3 | Integrating heterogeneous medical data |
| 4 | Trustworthy AI in healthcare |
| 6 | Test/review, final presentation |
Assessment Method:
Homework and projects.