We've finished the next module! www.dmsussman.org/assets/teach... starts with a linear congruential generator and ends with Bayesian inference of model parameters via Hamiltonian Monte Carlo. Quite a journey!

Thanks for the kind words!

The course assignments use GitHub with autograded tests. I'll make them all public at the end of the semester, but if you're interested I'm happy to share those, too -- just reach out!

We're in the middle of a module on randomness as a computational resource (hence the Caravaggio painting on the website at the moment). We'll go from simple PRNGs to Metropolis Monte Carlo to using HMC for Bayesian inference. I'll be adding those notes to the webpage as they're completed.

www.dmsussman.org/assets/teach... dives into ODEs, starting with the humble Euler's method. The module's focus is on building a modular N-body framework, and progresses from RK methods to the physics of symplectic integrators and shadow Hamiltonians, and ends with an intro to molecular dynamics.

Running in parallel with the language intro, www.dmsussman.org/assets/teach... discusses the craft of computational science: version control with Git, sane project architecture, and designing reproducible experiments (skills I wish we taught more explicitly to grad students everywhere!)

www.dmsussman.org/assets/teach... is a hands-on intro to Julia, using the problem of calculating pi in increasingly unusual ways as a running theme. It culminates in a method from a physics paper that is one of the few calculations to ever make me laugh out loud.

I made the somewhat idiosyncratic choice of teaching the course in #JuliaLang -- "idiosyncratic" as it was a language that I didn't know myself at the start of the year. My rationale for this (and course design choices) is in the preface.
Crazy, or crazy like a fox?
www.dmsussman.org/assets/teach...

During the pandemic I taught a course (www.dmsussman.org/teaching/phy...) loosely based on Bloomfield's textbook. If I did it again I think I would try to make it a bit less traditional, but it was an easy place to start.

A long derivation with a beautiful payoff! Also --- non-standard notation for the polylog, or just a notation I'm not used to?