Comparative Literature · Spring 2027
SDSU COMP 536 — Sci Modeling Machine Learning
Advanced computational methods and machine learning using Python, implemented from mathematical foundations. Monte Carlo methods, dynamical simulations, Bayesian inference, MCMC, Gaussian processes, neural networks with backpropagation. Students build research-grade implementations, transition to modern frameworks. Develops AI literacy and responsible use. Emphasizes professional software practices and performance optimization.
Course requirements
Prerequisites: MATH 252; MATH 254 or MATH 342A (or equivalent with instructor consent); or Graduate standing
- MATH 252; MATH 254 or MATH 342A (or equivalent with instructor consent); or Graduate standing
Spring 2027 enrollment options
Seat status can change| Class # | Format | Status | Seats | Time | Location | Professor | RateMyProfessors |
|---|---|---|---|---|---|---|---|
| 4124 Discussion | In person | Open | 0 / 30 | Mon/Wed 5:30 PM–6:45 PM | PS 140 | Anna Rosen | 1.9/5 · 11 reviews Difficulty 3.9 · 9% take again |