Numerical Physics and Machine Learning
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.
- Alberto ROSSO (LPTMS, CNRS et Université Paris-Sud, Orsay)
- Guillaume ROUX (LPTMS, CNRS et Université Paris-Sud, Orsay)
- Marcello CIVELLI (LPS, Université Paris-Sud, Orsay)
- 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
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- SMAC W. Krauth Statistical Mechanics: Algorithms and Computations (Oxford: Oxford University Press) (2006)
- Other references are specified in each lectures