# master international Physics of Complex Systems

## Computational Sciences, Florent Krzakala

You can find on overleaf the lecture
notes written by the previous student for my course in the
master
Physics of Complex Systems. Feel free to edit them and to
correct mistakes!

- Chap 1: probability theory 101

The first lecture is an introduction to probability theory, classical bounds and large deviation theory.
The first Homework is posted:
see the file on
overleaf. It is due for october 5 (no delay accepted).

Very basic examples of python notebook relevant to Homework 1 are
given on my github page, see for
instance: for
a pooling problem
or for
a sampling one

- Chap 2: a primer on statistics

The second lecture is an to statistics inference, consistant estimation, maximum likelihood etc...
The second lecture is an to sampling simple distributions
The second Homework is posted:
see the file on
overleaf. It is due for october 26 (no delay accepted).

- Chap 4: Monte-Carlo Markov-Chain

The next lecture is on monte-carlo markov chains. The coursera lecture of Werner Krauth is a very good reference, including this and videos.

The third Homework is posted:
see the file on
overleaf. It is due for November 15 (no delay accepted).

The last set of lecture will focus on machine learning technics.
The fourth Homework is posted: see overleaf. It is due before the end of the calendar year.

A good book for probability and statistics, accessible to students, is Larry A. Wasserman 's All of Statistics

Monte-Carlo methods are well covered in Werner Krauth's Statistical Mechanics: Algorithms and Computations. His MOOC on coursera is also recommended.

A good introduction to statistical learning is given in Elements of Statistical Learning by Jerome H. Friedman, Robert Tibshirani, and Trevor Hastie.

Another great reference is Machine Learning:A Probabilistic Perspective, by Kevin P. Murphy.

Exam from 2018: exam

Exam from 2017: exam