Lectures

COMP 536

Author

Anna Rosen

Published

April 22, 2026

Course Format

COMP 536 consists of lectures on Mondays and lab sections on Wednesdays. Lecture slides and materials are posted here after each class.

Lecture Schedule

Python Fundamentals (Weeks 1-3)

Week Topic Slides Reading
1 Course Overview & Python Environment TBD Ch 1
2 Modular Design & OOP Slides Ch 5-6
3 NumPy & Matplotlib Slides Ch 7-8

Numerical Methods (Weeks 4-7)

Week Topic Slides Reading
4 ODE Solvers: Euler & RK2 Slides Module 3, Parts 1-3
5 Symplectic Integrators & N-Body TBD TBD
6 Statistical Thinking Foundations Slides Module 1 Overview
7 Root Finding & Optimization TBD TBD

Monte Carlo Methods (Weeks 8-10)

Week Topic Slides Reading
8 Random Sampling & Monte Carlo Basics TBD TBD
9 Monte Carlo Radiative Transfer TBD TBD
10 Variance Reduction Techniques TBD TBD

Statistical Inference (Weeks 11-13)

Week Topic Slides Reading
11 Bayesian Inference & MCMC TBD TBD
12 Model Fitting & Parameter Estimation TBD TBD
13 Gaussian Processes TBD TBD

Modern Scientific Computing (Weeks 14-16)

Week Topic Slides Reading
14 JAX & Automatic Differentiation TBD TBD
15 Neural Networks & Emulators TBD TBD
16 Final Project Due — —

Lab Sessions (Wednesdays)

Lab time is for hands-on work on projects. Come prepared having reviewed the current project requirements. Bring your laptop.

Expectations:

  • Review project requirements before Wednesday’s lab
  • Use lab time for focused, productive work
  • Collaborate with classmates (while ensuring submissions reflect your own understanding)
  • Ask questions and get help from the instructor

Accessing Materials