This course will present various advanced computational topics in neural networks and/or machine learning, depending on the year and the interest of the students. Most of these topics require insights from statistical mechanics and stochastic processes.
Date | Topic | Material | Exercises | |
1 | May 18 | Introduction neurons and brain Networks of binary neurons; Markov process; ergodicity |
Reader Introduction Biophysics chapter 2 Reader Computational physics chapter 2 |
Section 2.5 |
2 | May 25 | Discussion of exercises Boltzmann-Gibbs distribution Mean field theory; Linear response theory |
Reader Computational physics chapter 3 | Section 3.7 1-3 |
3 | June 1 | No class | ||
4 | June 8 | Discussion exercises ch 3 Linear response theory |
Reader Computational physics chapter 3 |
Computer exercise 3.7 4 on mean field theory
initial matlab code |
5 | June 15 | Discussion exercises ch 3.7 4 Inverse Ising models for brain modeling |
Schneidman et al., Nature 2006 Roudi, Tyrcha and Hertz 2009 |
|
6 | June 22 |
Bayesian reasoning Simple expert system MCMC methods |
MacKay chapter 2, 29 | Computer exercise combinatorial optimization |