BME1560.pdf

BME1560 Artificial Intelligence for Biomedical Engineering (Fall 2022)
Course Description: This course provides an opportunity to graduate students with both the breadth and depth in the area of machine learning and deep learning applied to biomedical applications.
Learning Outcomes:
- Develop the research intuition to use and develop machine / deep learning approaches pertaining to specific biomedical engineering problem.
- Learn deep learning strategies to work with different data types, including images, videos, sensors, text and speech.
- An in-depth understanding of supervised, unsupervised, and self-supervised deep learning methods.
- Prepare constructive paper critiques that is clear and concise by identifying the strengths of the research papers and highlighting their potential shortcomings.
- Improve upon the methodology, experimental design and rigorous evaluation of various deep learning methods.
- Enhance programming abilities by implementation in python and PyTorch framework.
- Formulate research questions, work collaboratively on a group project, prepare an academic style written report, and give a presentation to the class.
Grading:
Participation: 15%
Assignments: 15%
Paper Critiques: 20%
Course Project: 50%
Total: 100%
Course Format: This course is a participatory course and has two components – taught lectures and seminar style paper reading.
Contact: Dr. Shehroz Khan, [email protected].