Machine Learning for Science
Fall 2025, CMU 10744


Lectures: TR, 12:30-1:50pm, HH B131

Instructor: Leila Wehbe

Communication: Canvas will be used for discussion about the course and assignments.

Course Description

This course will cover important topics that surround the use of Machine Learning to create scientific models and make scientific inferences. This includes methodological domains such as robustness and reproducibility, statistical testing, model interpretability, post-selection inference, tradeoff of simplicity and predictivity, learning from scratch vs. implementing domain knowledge, the increasing use of deep learning to make scientific breakthroughs, etc. We will evaluate these topics in general as well as applied to specific scientific fields. We will also interact with a wide array of scientists from different disciplines on campus to understand how Machine Learning is currently applied in different domains of science (genomics, physics, chemistry, neuroscience, epidemiology, etc).

The course will include readings, discussions, interviews, weekly short quizzes and two midterm exams.

Learning Objectives:

The aim of this class is to enable students to:
  • develop an understanding of the methodological and procedural considerations related to applying machine learning for scientific inquiry,
  • develop critical thinking in analyzing and interpreting machine learning results,
  • survey recent breakthroughs in science that rely on machine learning and AI,
  • connect with researchers on campus.

Prerequisites

Any version of intro to machine learning or similar class (10301, 10315, 10405, 10601, 10605, 10701, 10715). If you believe your background from other coursework or experience is equivalent, please contact the instructor.

Schedule

Tentative schedule, might change according to class progress and interest.

Grading

The course is a 12 unit class. The aim is to focus on the material discussed in class, and avoid multi-tasking during class. The graded components are as follows:
  • 50% lecture quizzes. Each lecture, a very short quiz will be administered on the materials from the last lecture, with open handwritten notes allowed (the 20 best scores will be counted towards the grade).
  • 30% Midterms. Two in-class exams (one in the middle and one at the end of the semester. No final scheduled after the end of classes).
  • 20% Participation in class discussion and in discussions with invited speakers.

Course Policies

Screens

Most of the work in this class will happen in class. You are discouraged from trying to do other work in class. Laptops and other screens are also discouraged. They can really interfere with your focus and learning. If you must use a laptop, I ask you to sit in the back or sides of the class to avoid distracting others.

Attendance

This course is a discussion-based course and therefore attendance is essential for benefiting from it and contributing to it. Attendance will not be directly graded but every lecture there will be a quiz, and participation will be recorded. We understand that some events such as conferences will lead to absence. Please communicate your absence in advance to the course staff.

Collaboration

Discussion of class material is heavily encouraged. Obviously, collaboration in quizzes and exams is not allowed.

Academic Integrity

We have a zero tolerance policy for violation of class policies. If you are in any doubt in regards to the policy, please clarify with the course staff before proceeding.

Take care of yourself

Take care of yourself. Do your best to maintain a healthy lifestyle this semester by eating well, exercising, avoiding drugs and alcohol, getting enough sleep and taking some time to relax. This will help you achieve your goals and cope with stress.

All of us benefit from support during times of struggle. There are many helpful resources available on campus and an important part of the college experience is learning how to ask for help. Asking for support sooner rather than later is almost always helpful.

If you or anyone you know experiences any academic stress, difficult life events, or feelings like anxiety or depression, we strongly encourage you to seek support. Counseling and Psychological Services (CaPS) is here to help: call 412-268-2922 and visit their website at http://www.cmu.edu/counseling/. Consider reaching out to a friend, faculty or family member you trust for help getting connected to the support that can help.

If you or someone you know is feeling suicidal or in danger of self-harm, call someone immediately, day or night:
  • CaPS: 412-268-2922
  • Re:solve Crisis Network: 888-796-8226

If the situation is life threatening, call the police
  • On campus: CMU Police: 412-268-2323
  • Off campus: 911