Course Number: 02-518
Modern medical research increasingly relies on the analysis of large patient datasets to enhance our understanding of human diseases. This course will focus on the computational problems that arise from studies of human diseases and the translation of research to the bedside to improve human health. The topics to be covered include computational strategies for advancing personalized medicine, pharmacogenomics for predicting individual drug responses, metagenomics for learning the role of the microbiome in human health, mining electronic medical records to identify disease phenotypes, and case studies in complex human diseases such as cancer and asthma. We will discuss how machine learning methodologies such as regression, classification, clustering, semi-supervised learning, probabilistic modeling, and time-series modeling are being used to analyze a variety of datasets collected by clinicians. Class sessions will consist of lectures, discussions of papers from the literature, and guest presentations by clinicians and other domain experts. Grading will be based on presentations, assignments, participation, and a project.
- Homeworks (60%)
- Four homeworks will be assigned.
- Every student is given a budget of three 'late days' to use as they see fit.
- There is no need to email the instructor to request a late day
- Anything that is more than 15 minutes past the deadline is considered late.
- If you hand the assignment more than 24 hrs after the deadline, two late days will be changed, etc.
- Once you use up your three late days, a 25% per day penalty is applied to the homework grade.
- Quizzes (40%)
- Six quizzes will be given online via Canvas.
Prerequisite(s): The course is designed for graduate and upper-level undergraduate students with a wide variety of backgrounds. Students should have some background in Machine Learning, but no prior background in Medicine is required.