AM 160Introduction to Scientific Machine Learning
Introduction to scientific machine learning covering dimension reduction techniques for scientific data, modern methods in sparse regression and compressed sensing, deep neural networks for modeling real-life systems, and neural ordinary differential equations.
Prerequisite(s): AM 20 and AM 30, or MATH 24, or PHYS 116A, and AM 129 or CSE 30. Enrollment is restricted to junior and senior students, and graduate students in applied mathematics. Prerequisite courses waived for graduate students.
- Enroll in lecture and associated discussion section. Taught in conjunction with AM 261.
Find a section and add it to your scheduleLive seat counts, time-conflict checks and the walk from your previous class.
When it runs
Winter 2027Open Winter 2027
| Section | Type | When | Where | Instructor | Seats |
|---|---|---|---|---|---|
| 01 | Lecture | Asynchronous | — | Staff | Open 0/40 |
Winter 2026Open Winter 2026
| Section | Type | When | Where | Instructor | Seats |
|---|---|---|---|---|---|
| 01 | Lecture | Mon Wed Fri 1:20pm–2:25pm | Soc Sci 2 071 | A. Chattopadhyay | Open 30/50 |
Who teaches it
| Instructor | Rating | Difficulty | Would take again | Reviews |
|---|---|---|---|---|
| Ashesh Chattopadhyay | 3.3 / 5 | 2.8 / 5 | 70% | 33 |
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