Machine Learning for Health

Basic Science Research Track, Clinical Research Training Program
Duke University School of Medicine

Course Director: Matthew Engelhard

This site is intended to accompany the main course site in Talent LMS

Course Materials

• Please review the syllabus by clicking here
• Materials for each week are linked in the Schedule below

Readings and Post-Reading Surveys

• A weekly reading + survey will be due before each class (with exceptions noted in the Schedule)
• Readings are linked below, and surveys must be completed in Talent LMS
• Both are pass/fail and together are a substantial portion (20%) of your final grade

Computational Exercises

• Weekly computational exercises will be due before class (again with a few exceptions, as noted in the Schedule)
• Each exercise will present code and output, then ask you to modify the code to complete additional tasks
• These exercises require you to code in Python in a Jupyter Notebook environment
• We recommend either installing Anaconda or working in Google Colab.
• Recommended Python resources include Duke Library tutorials, Python Crash Course, and Google Python class

Final Project

• The course will culminate in a project in which you apply data science methods to a clinical dataset of your choosing.
• Project instructions and grading details are here.
• Proposals are due before class in week 6, and the project is due before the final class period.

Schedule

• Please follow the schedule and activities posted in Talent LMS