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 rigour will be avoided.
The course is aimed at the DSSC track. Clashes with other courses in this track should be avoided, as well as clashes with Classics in Evolutionary Biology and Bioinformatics.
Christoph Lampert may offer a “Probabilistic models” course that emphasizes graphical models & discrete processes. This is complementary.
I don’t have a strong preference as to timing, except that Spring 2 I will likely be away on fieldwork, and in fall 2, some students might not have completed necessary preliminaries, such as Linear Algebra.
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: 2020
Track segment(s):
DSSC-PROB Data Science and Scientific Computing - Probabilistic Models
Teacher(s):
Nicholas Barton
Teaching assistant(s):
Ksenia Khudiakova
If you want to enroll to this course, please click: REGISTER
- Teacher: Nick BARTON