Computational methods (both numerical and symbolic) are ubiquitous in modern physics. Such methods are usually well-suited for self-teaching and independent experimentation, largely because of the wealth of resources available online. Learning how to research and implement algorithms independently is superior to following rote instructions. The purpose of this course is to introduce some key topics and provide the background and support needed for students to research and explore their own implementations.
The fall term will focus on classical and statistical simulations. The winter term will focus on a variety of other topics, including random matrices, symbolic algebra, and convex optimization.
Instructor: David Simmons-Duffin, Lauritsen 442, email: dsd.
TA: Fall: Brenden Roberts (broberts).
Offered: Fall and winter terms, 2019-2020.
Class meetings: This is a project-based course. There are no official lectures. Lab hours will be set by a poll at the beginning of the term. If you are enrolled in or auditing the course, please join the moodle to ensure you get announcements.
Grading and homework: This course is pass/fail. There will be four projects over the course of the term.
Prerequisites: Students should be comfortable with the command line and familiar with at least one of Python/C/C++.