Learning Markov Random Fields and Models of Statistical Physics from Data
5
Algorithms and machine learning
Advanced studies
The aim of this course is to explore the connection between Machine Learning and Statistical Physics.
Exam
18.12.2015
16.00
B123
Year | Semester | Date | Period | Language | In charge |
---|---|---|---|---|---|
2015 | autumn | 28.10-31.12. | 2-2 | English | Onur Dikmen |
Lectures
Time | Room | Lecturer | Date |
---|---|---|---|
Wed 14-16 | B222 | Onur Dikmen | 28.10.2015-11.12.2015 |
Fri 14-16 | B222 | Onur Dikmen | 28.10.2015-11.12.2015 |
Information for international students
The course will be taught in English.
General
The course introduces undirected graphical models which are widely used in physics, biology, image processing, and many more disciplines, and teaches you how to estimate them from data.
Lecturers
Content
- Background on statistical inference (likelihood function, Bayes theorem ...)
- Ising models / Boltzmann machines and other Markov random fields
- Reasons why exact inference is difficult for Markov random fields
- Approximate inference methods (Monte Carlo methods, pseudo-likelihood, variational Bayes, ...)
- Applications
- Deep learning
Prerequisites
A good understanding of Linear Algebra, Calculus and Probability. Familiarity with a programming language (e.g. Matlab, Python, R,...)
Grading
Regular exams: Homework 40%, exam 60%.
Separate exams: Homework 0%, exam 100%.
Material