For any questions or concerns about the course, please contact Kyle Liu ([email protected]) and/or Sanna Madan ([email protected]).
Fridays, 2:00-2:50 PM
CSIC 2118
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.
- A few useful things to know about machine learning by Pedro Domingos
- Data Science Cheatsheet by Maverick Lin
- Supervised Learning Cheatsheet by Stanford CS 229 Machine Learning
- Secondary Analysis of Electronic Health Records by MIT Critical Data
- 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 |
| 13 (11/23) | THANKSGIVING BREAK | |
| 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.
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).
Instructor:
Dr. Max Leiserson - [email protected]
TA(s):
Kyle Liu - [email protected]
- Office Hours: MW 2:00 - 3:00PM in Startup Shell (387 Technology Dr.)
Sanna Madan - [email protected]
- Office Hours: MW 2:00-3:00PM in Startup Shell (387 Technology Dr.)
See the section titled "Attendance, Absences, or Missed Assignments" available at Course Related Policies.
See the section titled "Accessibility" available at Course Related Policies.
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 http://www.shc.umd.edu.
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.