This course introduces five topics in data analysis and simulation methods. The focus is on sampling and inferring probabilistic models. The course is organized around week-long or slightly longer modules, each covering a set of related approaches and consisting of 2 lectures, a recitation, and a problem set. The aim is for the students to both understand the methods, implement them, and try them out on real or simulated data. This is a hands-on course that should provide useful practical experience; the focus is not on formal rigor. The students may find the background of DSSC Track Core course helpful, but it is not required.

The central component of this course are problem sets which require a considerable amount of time to solve, but are essential in the learning-by-doing approach that we take.

Current topics run as follows:

1) Random numbers, Gillespie (SSA) simulation.
2) Monte Carlo and entropic sampling.
3) Probabilistic models, maximum likelihood / MAP inference.
4) Basics of information theory, linear vs information theoretic measures of dependency, redundancy, multi-information.
5) Density estimation, maximum entropy models, generalized linear models

Target group: Primarily DSSC students; possibly Physics students that require data analysis; biology students with strong formal or very good coding capability.

Prerequisites: (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).

Evaluation: 100% problem set (homework) assignments. Out of 5 homework assignments, students need to choose and turn in 3 assignments on time.

Teaching format: Blackboard lectures with some examples and literature reading, recitations to help with the homeworks and to present and discuss homework solutions.

ECTS: 3 Year: 2020

Track segment(s):
DSSC-ANA Data Science and Scientific Computing - Data Analysis
PHY-BIO Physics - Biophysics

Teacher(s):
Gasper Tkacik

Teaching assistant(s):
Michele Nardin

If you want to enroll to this course, please click: REGISTER