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 BoltzmannGibbs distribution Mean field theory; Linear response theory 
Reader Computational physics chapter 3  Section 3.7 13 
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 