Skip to content
CMSC389I Fall 2018 @ UMD
Jupyter Notebook
Branch: master
Clone or download
Fetching latest commit…
Cannot retrieve the latest commit at this time.
Type Name Latest commit message Commit time
Failed to load latest commit information.

For any questions or concerns about the course, please contact Kyle Liu ( and/or Sanna Madan (

CMSC389I: Disrupting Healthcare with AI

Prerequisites: C- or better in CMSC250 and CMSC216

Credits: 1

Time and Location

Fridays, 2:00-2:50 PM
CSIC 2118

Course Description

This course provides a comprehensive, practical introduction to the intersection of machine learning and different challenges in healthcare. Students will apply basic predictive modeling techniques to fields such as early detection of disease, telemedicine, and mental health. Prior knowledge of biology is not required but a basic understanding of Python and/or machine learning techniques is recommended.

List of Recommended Resources

Topics Covered

  • Machine Learning for Healthcare
  • Early Disease Detection
  • Electronic Health Records
  • Drug Discovery
  • Telemedicine
  • Mental Health


Grades will be maintained on ELMS.

You are responsible for all material discussed in lecture and posted on the class repository, including announcements, deadlines, policies, etc.

Your final course grade will be determined according to the following percentages:

Percentage Title Description
30% Quizzes We will regularly have quizzes in class based on readings from the previous week or in-class slides/lecture.
30% Codelabs Codelabs will be centered around analyzing specific datasets for a domain we cover in class.
40% Final Project The final project will be an original machine-learning model for a unique dataset in the healthcare domain. Students may draw inspiration from existing research papers, but must analyze the dataset themselves. All students will present their results at the end of the semester.

Any request for reconsideration of any grading on coursework must be submitted within one week of when it is returned. No requests will be considered afterwards.


Week Topic Assignment
1 (8/31) Syllabus Week + Intro to ML Algorithms Reading
2 (9/7) Machine Learning for Healthcare Quiz 1, Reading
3 (9/14) Early Disease Detection Quiz 2, Codelab 1 OUT
4 (9/21) Case Study: Early Disease Detection Quiz 3
5 (9/28) Electronic Health Records Quiz 4, Codelab 1 DUE
6 (10/5) Case Study: Electronic Health Records Quiz 5
7 (10/12) Drug Discovery Quiz 6, Codelab 2 OUT
8 (10/19) Case Study: Drug Discovery Quiz 7
9 (10/26) Tuning ML Models Codelab 2 DUE
10 (11/2) Differential Privacy Quiz 9
11 (11/9) Final Project Quiz 10, Final Project OUT
12 (11/16) Mental Health Quiz 11
14 (11/30) Guest Speaker + Presentation Prep
15 (12/7) Final Presentations Final Project DUE


The projects are meant to get you familiar with the techniques used to analyze healthcare datasets. Projects will focus on applying machine-learning models to domains such as early disease detection, electronic health records, and drug discovery. The projects will be implemented in Python for simplicity.

Outside-of-class communication with course staff

We will interact with students outside of class in primarily two ways: in-person during office hours and piazza. Email should only be used for emergencies and not class related questions (e.g., homework).


Dr. Max Leiserson -


Kyle Liu -

  • Office Hours: MW 2:00 - 3:00PM in Startup Shell (387 Technology Dr.)

Sanna Madan -

  • Office Hours: MW 2:00-3:00PM in Startup Shell (387 Technology Dr.)

Excused Absence and Academic Accommodations

See the section titled "Attendance, Absences, or Missed Assignments" available at Course Related Policies.

Disability Support Accommodations

See the section titled "Accessibility" available at Course Related Policies.

Academic Integrity

Note that academic dishonesty includes not only cheating, fabrication, and plagiarism, but also includes helping other students commit acts of academic dishonesty by allowing them to obtain copies of your work. In short, all submitted work must be your own. Cases of academic dishonesty will be pursued to the fullest extent possible as stipulated by the Office of Student Conduct.

It is very important for you to be aware of the consequences of cheating, fabrication, facilitation, and plagiarism. For more information on the Code of Academic Integrity or the Student Honor Council, please visit

Course Evaluations

If you have a suggestion for improving this class, don't hesitate to tell the instructor or TAs during the semester. At the end of the semester, please don't forget to provide your feedback using the campus-wide CourseEvalUM system. Your comments will help make this class better.

Thanks to the writers of this syllabus for the wording of much of this document.
You can’t perform that action at this time.