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= E2 334 : Topics in Computation over Networks, Spring 2019

== Lectures
- 03 Jan 2019: Lecture-01 [2019/statphy/lecture-01.pdf Random Variables and Entropy]
- 08 Jan 2019: Lecture-02 [2019/statphy/lecture-02.pdf Joint Entropy and Mutual Information]
- 10 Jan 2019: Lecture-03 [2019/statphy/lecture-03.pdf Data Processing]
- 12 Jan 2019: Lecture-04 [2019/statphy/lecture-04.pdf Data Compression and Transmission]
- 15 Jan 2019: Lecture-05 [2019/statphy/lecture-05.pdf The Boltzmann Distribution]
- 17 Jan 2019: Lecture-06 [2019/statphy/lecture-06.pdf The Fluctuation-Dissipation Theorem]
- 21 Jan 2019: Lecture-07 [2019/statphy/lecture-07.pdf The Thermodynamic Limit]
- 25 Jan 2019: Lecture-08 [2019/statphy/lecture-08.pdf Ising Model: One-dimensional case]
- 29 Jan 2019: Lecture-09 [2019/statphy/lecture-09.pdf Curie-Weiss Model]
- 31 Jan 2019: Lecture-10 [2019/statphy/lecture-10.pdf Independent random variables]
- 05 Feb 2019: Lecture-11 [2019/statphy/lecture-11.pdf Correlated random variables]
- 07 Feb 2019: Lecture-12 [2019/statphy/lecture-12.pdf The Gartner-Ellis theorem]
- 12 Feb 2019: Lecture-13 [2019/statphy/lecture-13.pdf The Monte Carlo method]
- 14 Feb 2019: Lecture-14 [2019/statphy/lecture-14.pdf Total variation distance]
- 19 Feb 2019: Lecture-15 [2019/statphy/lecture-15.pdf Distance from stationarity]
- 21 Feb 2019: Lecture-16 [2019/statphy/lecture-16.pdf Mixing times]
#- 01 Mar 2019: Lecture-17 [2019/statphy/lecture-17.pdf Reversibility]
#- 06 Mar 2019: Lecture-18 [2019/statphy/lecture-18.pdf Reversed Processes]
#- 08 Mar 2019: Lecture-19 [2019/statphy/lecture-19.pdf Stochastic Networks]
#- 13 Mar 2019: Lecture-20 [2019/statphy/lecture-20.pdf Martingales]
#- 15 Mar 2019: Lecture-21 [2019/statphy/lecture-21.pdf Martingale Convergence Theorem]
#- 20 Mar 2019: Lecture-22 [2019/statphy/lecture-22.pdf Martingale Concentration Inequalities]
#- 22 Mar 2019: Lecture-23 [2019/statphy/lecture-23.pdf Exchangeability]
#- 27 Mar 2019: Lecture-24 [2019/statphy/lecture-24.pdf Random Walks]
#- 29 Mar 2019: Lecture-25 [2019/statphy/lecture-25.pdf Random Walks: GI\/G\/1 Queue]
#- 03 Apr 2019: Lecture-26 [2019/statphy/lecture-26.pdf Reversible Markov Chains]
#- 05 Apr 2019: Lecture-27 [2019/statphy/lecture-27.pdf Reversible Markov Chains]
#- 10 Apr 2019: Lecture-28 [2019/statphy/lecture-28.pdf Reversible Markov Chains]

== Homeworks
#- 24 Jan 2019: [2019/statphy/homework-01.pdf Homework-01] Due Friday, Jan 25 [2019/statphy/homework-01-soln.pdf Solutions].
#- 30 Jan 2019: [2019/statphy/homework-02.pdf Homework-02] Due Friday, Feb 09 [2019/statphy/homework-02-soln.pdf Solutions].
#- 15 Feb 2019: [2019/statphy/homework-03.pdf Homework-03] Due Friday, Feb 23 [2019/statphy/homework-03-soln.pdf Solutions].
#- 03 Mar 2019: [2019/statphy/homework-04.pdf Homework-04] Due Friday, Mar 09 
#- 19 Mar 2019: [2019/statphy/homework-05.pdf Homework-05] Due Friday, Mar 23
#- 25 Mar 2019: [2019/statphy/homework-06.pdf Homework-06] Due Friday, Apr 06
#- 31 Mar 2019: [2019/statphy/homework-07.pdf Homework-07] Due Friday, Apr 13 [2019/statphy/homework-07-soln.pdf Solutions]


== Grading Policy
Scribe : 50\n
Project: 50\n

== Course Syllabus
Content will be a subset of the following topics:
- *Statistical physics:* 
Boltzmann distributions, Thermodynamic potentials and limit, Ferromagnets and Ising models
- *Probability:* 
Stochastic ordering, large deviations, Gibbs free energy, Monte Carlo method, simulated annealing
- *Independence:* 
Random energy model, random code ensemble, number partitioning, replica theory
- *Graph models:* 
Random factor graphs, Random K-SAT, LDPC codes
- *Phase transitions:* 
Erdos Renyi random graph
- *Short-range correlations:* 
Belief propagation, Ising models on random graphs
- *Long range correlations:* 
Cavity method
#[PDF]

== Course Description
A large number of local microscopic interactions can lead to many interesting macroscopic physical phenomena. 
These effects have been observed in physical systems, and statistical physics presents models that can describe such effects. 
In this course, we will learn the techniques from statistical physics to describe complex network behaviors. 

== Prerequisite
First graduate course in probability from any engineering or math department. 
Familiarity with information and coding theory is desired, though not necessary to attend the course.  

== GitHub/Slack Information
=== Slack
Students can signup for course slack using their iisc.ac.in email at [https://courses-ece-iisc.slack.com/signup Slack signup]. 
Add yourself to \#statphy-2019.

=== GitHub
All the students in the class have read/write access to [https://github.com/TeachingReps/Statistical-Physics Stastistical-Physics] public repository on GitHub.\n 
Please follow the guidelines in the [https://github.com/TeachingReps/Stochastic-Processes/blob/master/sampleLecture.pdf sample lecture].\n
The source file for the [https://github.com/TeachingReps/Stochastic-Processes/blob/master/sampleLecture.tex sample lecture] is in the repository.\n
#It is recommended you save it with another name in your local repository for creating a new lecture.\n
A good book for Git is [https://git-scm.com/book/en/v2 here] and a simple tutorial [http://readwrite.com/2013/09/30/understanding-github-a-journey-for-beginners-part-1 here].\n

== Instructor

[../ Parimal Parag]\n
Office: EC 2.17 \n
Hours: By appointment. 

== Time and Location
Classroom: EC 1.07, Main ECE Building \n
Hours: Tue/Thu 08:30-10:00am.

#== Teaching Assistant
#Rahul Ramachandran \n
#Email: rrahul@iisc.ac.in \n

== References 
- [https://ieeexplore.ieee.org/document/910572 Factor Graphs and the Sum-Product Algorithm], Frank Kschischang, Brendan J. Frey, Hans-Andrea Leliger. /IEEE Transactions on Information Theory/. Vol. 47, no. 2, 2001.
- [https://ieeexplore.ieee.org/document/825794 The Generalized Distributive Law], S.M. Aji,  R.J. McEliece. /IEEE Transactions on Information Theory/. Vol. 46, no. 2, pp. 325--343, 2000.

== Textbooks

- [https://web.stanford.edu/~montanar/RESEARCH/book.html Information, Physics, and Computation], Mezard, Montanari, 2009. \n
- [https://www.win.tue.nl/~rhofstad/NotesRGCN.pdf Random graphs and complex networks], Remco van der Hofstad, 2018. \n