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COMP 536 - Scientific Modeling and Machine Learning

COMP 536: Scientific Modeling and Machine Learning (3 units) at San Diego State University. 1 section offered in Spring 2027, with meeting times, instructors, and seat counts. Prerequisites: MATH 252; MATH 254 or MATH 342A (or equivalent with instructor consent); or Graduate standing. Counts toward Computational Science, Data Science Concentration, M.S. (2026-27), Computational Science, Data Science Concentration, M.S. (2025-26).

3 units
  • Required Core Courses (18 Units)
  • Required Core Courses
  • Required Core Courses (24 Units)
  • At Least Nine Units Selected from
  • Required Core Courses (15 Units)
Offered: Summer 2027 (not offered), Spring 2027 (offered), Fall 2026 (unknown)
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.
Prerequisites
MATH 252; MATH 254 or MATH 342A (or equivalent with instructor consent); or Graduate standing

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Who can take it
MATH 252; MATH 254 or MATH 342A (or equivalent with instructor consent); or Graduate standing
Grading
LCR: Letter Grade with Cr/NC available. The grading default for the class will be letter grade, but students can opt to take it for Cr/NC
Typically offered
Spring

1 section in Spring 2027

Class #4124· In-person · Open · Letter or CR/NC
30 of 30 seats open
Mon, Wed 5:30 PM-6:45 PM · PS 140

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