Winter 2019 edition
Course overview
Jan 07 (M) - Introduction to PHYS 177
Jan 09 (W) - Python, Jupyter, and GitHub
Jan 11 (F) - Numerical precision
Numerical integration (of integrals)
Jan 14 (M) - Riemann sum and trapezoidal rule
Jan 16 (W) - Simpson’s rule
Jan 18 (F) - Error estimation
Numerical integration (of differential equations)
Jan 21 (M) - MLK Jr Day, no class
Jan 23 (W) - Euler’s method
Jan 25 (F) - Runge-Kutta method
Jan 28 (M) - Leapfrog and other approaches
Statistical mechanics
Jan 30 (W) - Probability and random numbers
Feb 01 (F) - Computing with probability distributions
Feb 04 (M) - Statistical physics and probability I
Feb 06 (W) - Statistical physics and probability II
Feb 08 (F) - The Ising model
Feb 11 (M) - Markov chain Monte Carlo
Feb 13 (W) - Monte Carlo simulation for the Ising model
Optimization
Feb 15 (F) - Convex optimization and steepest descent
Feb 18 (M) - President’s Day, no class
Feb 20 (W) - Midterm presentations
Feb 22 (F) - Line search
Feb 25 (M) - Newton’s method
The schedule below this point is preliminary!
Statistical inference
Feb 27 (W) - Loss functions and linear regression
Mar 01 (F) - Likelihood-based inference
Mar 04 (M) - Bayesian inference
Mar 06 (W) - No class
Data handling/visualization/machine learning
Mar 08 (F) - Tidy data
Mar 11 (M) - Perceptron I
Mar 13 (W) - Perceptron II
Final exam
Mar 15 (F) -