The course will cover basic stochastic processes, emphasizing examples from a range of fields. This will include Markov chains, branching processes, and the diffusion approximation. Mathematical rigor will be avoided.

Target group: Students with good mathematical and computational ability. Appropriate for students interested in data science, population genetics, statistical physics, etc.

Prerequisites: Linear algebra, and some basic knowledge of probability.

Evaluation: Homework (no exam)

Teaching format: Lectures, problems classes

ECTS: 3 Year: 2019

Track segment(s):
DSSC-PROB Data Science and Scientific Computing - Probabilistic Models

Teacher(s):
Nicholas Barton

Teaching assistant(s):
Himani Sachdeva

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