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Revision as of 12:54, 31 August 2020 by Alberto (talk | contribs) (Course description)

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Numerical Physics and Machine Learning

Course description

We will cover many algothims used in many-body problems and complex systems: Monte Carlo methods, molecular dynamics and optmization in complex landscapes. We shall also discuss the use of some machine learning algorithms (Boltzmann machines, Auto-encoder, Deep Learning) for physics problems. We focus on algorithms and physics, not on programming and heavy numerics. The theoretical lecture is followed by a tutorial introducing concrete numerical exercises. You will have to hand in 3 homeworks.



  • Lectures on Fridays: 14.0-16.00
  • Tutorials on Fridays: 16h00-18.00
  • ENS, 24 rue Lhomond, room Conf IV (2nd floor)

Here you find the scheduling of Lectures, Tutorials and Homeworks


The working language for this course is English.
Programming Language: Python 3. See Memento Python
No previous experience in programming is required.
You need first of all to have Python installed with at least modules NumPy, SciPy and matplotlib.


3 homeworks (10 points each) + 1 MCQ (20 points) + 1 oral exam (50 points)


The WIFI network is: PHYS-GUEST

The WIFI password for this networks is: PhysiqueENS

Then you can run a navigator (Firefox, Chrome, IE, Safari...)

Accept the certificat and enter the password:


here it is please register