Course Schedule and Catalog
Course Schedule - Spring Semester 2027
Meeting location information can now be found on student schedules in ESTHER (for students) or on the Course Roster in ESTHER (for faculty and instructors).Additional information available here.
Meeting location information can now be found on student schedules in ESTHER (for students) or on the Course Roster in ESTHER (for faculty and instructors).Additional information available here.
BIOS 365 001 (CRN: 26636)
MACHINE LEARNING BIOCHEMISTRY
Long Title: MACHINE LEARNING FOR BIOCHEMISTRY
Department: Biosciences
Instructors:
Meeting: 4:00PM - 5:15PM TR (11-JAN-2027 - 23-APR-2027)
Part of Term: Full Term
Grade Mode: Standard Letter
Course Type: Lecture
Language of Instruction: Taught in English
Method of Instruction: Face to Face
Credit Hours: 3
Course Syllabus:
Course Materials: Rice Campus Store
Restrictions:
Must be enrolled in one of the following Level(s):
Undergraduate Professional
Visiting Undergraduate
Undergraduate
Prerequisites: (MATH 101 AND MATH 102) OR (MATH 105 AND MATH 106) AND (STAT 305 OR STAT 315) AND BIOS 301
Section Max Enrollment: 10
Section Enrolled: 0
Waitlisted: 0 (Max 99)
Current members of the waitlist have priority for available seats.
Enrollment data as of: 2-NOV-2026 7:18AM
Final Exam: No Final Exam
Description: Practical introduction to machine learning for biomolecular science. Students progress from regression and classification to autoencoders, convolutional and graph neural networks, transformers, protein language models, structure prediction, and reinforcement learning using pretrained models and Python workflows. Emphasis on data curation, baseline selection, rigorous evaluation, reproducibility, and a semester-long research project. Recommended Prerequisite(s): Recommended preparation: familiarity with Python and introductory coursework in linear algebra, probability, or statistics. Prior coursework in machine learning or deep learning is not required. The first three class meetings establish a shared computational baseline, but students should be prepared to learn and use Python throughout the semester.