Winter 2019 edition
Information for the course, including homework assignments and information about exams, will be posted here as we progress through the quarter.
Lecture 0 – Introduction to Physics 177 [slides]
Lecture 1 – Python, Jupyter, and GitHub [slides] [repository]
Lecture 2 – Numerical precision [slides] [repository]
Lecture 3 – Numerical integration: Riemann sum and trapezoidal rule [slides] [repository]
Lecture 4 – Numerical integration: Simpson’s rule [slides] [repository]
Lecture 5 – Numerical integration: Error analysis [slides] [repository]
Lecture 6 – Differential equations: Euler’s method [slides] [repository]
Lecture 7 – Differential equations: Runge-Kutta method [slides] [repository]
Lecture 8 – Random number generation [slides] [repository]
Lecture 9 – Calculations with probability distributions [repository]
Lecture 10 – Probability and statistical physics I [repository]
Lecture 11 – Probability and statistical physics II [slides] [repository]
Lecture 12 – The Ising model [slides] [repository]
Lecture 13 – Markov chain Monte Carlo [slides] [repository]
Lecture 14 – Practical MCMC [slides] [repository]
Lecture 15 – Convex optimization [slides] [repository]
Lecture 16 – Line search [slides] [repository]
Lecture 17 – Newton’s method [slides] [repository]
Lecture 18 – Loss functions and regression [slides] [repository]
Lecture 19 – Maximum likelihood [slides] [repository]
Lecture 20 – Bayesian inference [slides] [repository]
Lecture 21 – Data handling [slides] [repository]
Lecture 22 – Outlook [slides] [repository]
Follow the assignment links below to work on and submit each homework through GitHub Classroom. You can also view the repository for each homework directly.
Homework 1 – Introduction to Python, Jupyter, and Github [assignment] [repository]
Homework 2 – Numerical integration [assignment] [repository]
Homework 3 – Numerical integration of ordinary differential equations [assignment] [repository]
Homework 4 – Different approaches to numerical integration of differential equations [assignment] [repository]
Homework 5 – Statistical physics of a rubber band [assignment] [repository]
Homework 6 – Simulating the Ising model [assignment] [repository]
Homework 7 – Optimization for physics [assignment] [repository]
Homework 8 – Inference for the Ising model [assignment] [repository]
Some of the materials here are based on previous versions of the course taught by Flip Tanedo. You can find the 2017 and 2018 versions of the course at these links.