CHEM 151Machine Learning for Chemistry
Chemistry and Biochemistry · 4 units · Undergraduate upper division (100-199)
Introduction to machine learning and its many applications within chemical sciences. Topics include widely-used approaches for modeling large and complex data sets, including neural networks and deep learning, supervised and unsupervised learning, and dimensionality reduction. Exploration of mainstream applications of machine learning to problems of chemical interest, including molecular simulation and computer-aided drug and material design/discovery. Succinct introduction to linear algebra and programming in Python. Particular topics to be covered and projects to be completed may be decided in part based on student interest and input.
P/NP or letter grading.
When it runs
Not on the schedule for any of Fall 2025 through Spring 2027. UCLA publishes only that window, so this does not mean the course is gone — check the official listing.
Requisites
Official UCLA wording
Requisites: course 20B or 20BH, Mathematics 33A or 33AH.
Requires
Everything that has to come before this course, not just the courses named in the requisite above.
CHEM 151
- CHEM 20BChemical Energetics and Change
- CHEM 14AGeneral Chemistry for Life Scientists I
- LIFESCI 30AMathematics for Life Scientists
- CHEM 14AEGeneral Chemistry for Life Scientists I—Enhanced
- CHEM 20AHChemical Structure (Honors)
- CHEM 20BHChemical Energetics and Change (Honors)
- MATH 33ALinear Algebra and Applications
- MATH 33AHLinear Algebra and Applications (Honors)
4 direct requisites. The whole upstream is here — 31 courses over 3 levels. Every course here opens its own tree.
Unlocks
What this course is a requisite for, and what those courses lead to in turn.
No course in the catalog lists this as a requisite.