This course introduces a variety of data analysis and simulation methods. It is organized around week-long modules, each covering one method and consisting of 2 lectures, a recitation, and an extensive problem set. The aim is for the students to both understand the method and try it out on real or simulated data. This is a hands-on course that should provide useful practical experience. The students may find the background of DSSC Track Core course helpful, but it is not required.
Primarily DSSC students but open to any student with: (i) sufficient math background (linear algebra, basic calculus; typically at the level of intro Physics/CS/Engineering/Math undergrads); (ii) sufficient coding capability (working knowledge of a language that supports numerical computation, e.g., Matlab, Mathematica, C, Python, etc).
100% from problem set assignments, 6 in total.
|Oct 11||Random numbers, Monte Carlo integration||Mondi 1|
|Oct 13||Stochastic Simulation Algorithm||Mondi 1|
|Oct 18||Metropolis Monte Carlo and Ising systems||Mondi 1|
|Oct 20||Entropic sampling||Seminar room LBE|
|Oct 25||Basics of probabilistic inference||Mondi 1|
|Oct 27||Probabilistic inference, regularization||Mondi 1|
|Nov 3||Generalized Linear Models||Mondi 1|
|Nov 8||Introduction to information theoretic quantities||Mondi 1|
|Nov 10||Entropy and information estimation||Lakeside view room|
|Nov 15||Kernel density estimation, Gaussian Mixture Models||Mondi 1|
|Nov 17||Maximum entropy models||Mondi 1|
|Nov 22||Bayesian linear regression, feature space||Mondi 1|
|Nov 24||Gaussian Processes for regression||Mondi 1|
|See Week 1 Lecture notes below (Random numbers, SSA)||Oct 27|
|See Week 2 Lecture notes below (Metropolis MC)||Nov 3|
|See Week 3 Lecture notes below (Probabilistic Inference)||Nov 10|
|See Week 4 Lecture notes below (Information theoretic quantities)||Nov 24|
|See Week 5 Lecture notes below (Learning probability distributions)||Dec 1|
Week 2 Lecture notes and Homework
Week 6 Lecture Notes