BSCI238I: Machine Learning for the Life Sciences

Th 3:30 PM -4:20 PM @ ESJ B0322 | Fall 2026 | 1 Credit

Machine learning is transforming the life sciences, enabling breakthroughs in fields such as genomics, medical imaging, and drug discovery. This course introduces students to the fundamental principles of machine learning and its applications in biology and medicine.

Students will learn about key machine learning techniques, including classification, regression, clustering, and deep learning, with a focus on practical applications like cancer diagnosis, gene expression analysis, and protein structure prediction.

Through lectures and guided projects, students will gain an understanding of how to apply machine learning models to biological datasets, evaluate their performance, and interpret results. No prior programming or machine learning experience is required—this course is designed to bridge the gap between computational techniques and life science research, preparing biology students to understand and leverage machine learning as a tool as they encounter real-world challenges in their fields of study.

Syllabus

Fall 2026 Schedule

Date

Topic: HW

Sep 03

Introduction to Machine Learning: Assignment 1

Sep 10

Regression: Assignment 2

Sep 17

Guided Project 1: Regression: Finish Project 1 (if needed)

Sep 24

Classification: Assignment 3

Oct 01

Guided Project 2: Classification: Finish Project 2 (if needed)

Oct 08

Unsupervised Learning: Assignment 4

Oct 15

Guided Project 3: Unsupervised Learning: Finish Project 3 (if needed)

Oct 22

Deep Learning: Assignment 5

Oct 29

Convolutional Neural Networks and Large Language Models: Work on Final Project Proposal

Nov 05

Evaluating Machine Learning Models: Assignment 6, Work on Final Project Proposal

Nov 12

Guided Project 4: Deep Learning: Finish Project 4 (if needed)
FINAL PROJECT PROPOSAL DUE

Nov 19

Work Day / Office Hours: Work on Final Project

Nov 26

No class (Thanksgiving Break): Work on Final Project

Dec 03

Ethics and Challenges: Work on Final Project

Dec 10

Final Project Presentations: FINAL PROJECT DUE 12/11

Grading Breakdown

30%

Assignments
Short homeworks on lecture days, including problem sets and
conceptual questions.: 6 total

30%

Guided Projects
Guided python notebooks that apply lecture topics to real-world
biological datasets. Will be primarily completed in-class, with
opportunities for collaboration.: 4 total

30%

Final Paper/Project
Choice between two final projects: (1) A report presenting a
biological question of interest and quantitative analysis of a
relevant dataset using machine learning techniques, or
(2) A review paper analyzing 2 academic research articles
about a recent advancement in machine learning for
biological/medical applications.: 1 total

10%

Participation
Short attendance surveys completed after class, each worth 1%
of the final grade (with a maximum of 10%). Unexcused
absences will result in deductions from this grade.: 13 total