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= Numerical Physics and Machine Learning = | = Numerical Physics and Machine Learning = | ||
= Course description = | == Course description == | ||
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= Team = | == The Team == | ||
* [http://lptms.u-psud.fr/alberto_rosso/ Alberto Rocco] (Numerical Physics) | * [http://lptms.u-psud.fr/alberto_rosso/ Alberto Rocco] (Numerical Physics) | ||
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* Marco Medjnak (Tutorials) | * Marco Medjnak (Tutorials) | ||
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* Lectures on Fridays: 14.0-16.00 | * Lectures on Fridays: 14.0-16.00 | ||
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* ENS, 24 rue Lhomond, room Conf IV (2nd floor) | * ENS, 24 rue Lhomond, room Conf IV (2nd floor) | ||
< | = Computer Requirements = | ||
'''No previous experience in programming is required.''' <br> | |||
Programming Language: Python. For practical installation, we recommand (especially Anaconda. See [[Memento Python]] <br> | |||
Python notebooks on your computer are great. But another possibility, and quite good way to use powerful computer without buying one, is to use google colab, the Colaboratory platform from Google: It requires no specific hardware or software, and even allows you to use GPU computing for free, all by writting a jupyter notebook that you can then share. | |||
Revision as of 14:01, 31 August 2020
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.
The Team
- Alberto Rocco (Numerical Physics)
- Florent Krzakala (Machine Learning)
- Marco Medjnak (Tutorials)
Where and When
- Lectures on Fridays: 14.0-16.00
- Tutorials on Fridays: 16h00-18.00
- ENS, 24 rue Lhomond, room Conf IV (2nd floor)
Computer Requirements
No previous experience in programming is required.
Programming Language: Python. For practical installation, we recommand (especially Anaconda. See Memento Python
Python notebooks on your computer are great. But another possibility, and quite good way to use powerful computer without buying one, is to use google colab, the Colaboratory platform from Google: It requires no specific hardware or software, and even allows you to use GPU computing for free, all by writting a jupyter notebook that you can then share.
Grading
3 homeworks (10 points each) + 1 MCQ (20 points) + 1 oral exam (50 points)
Forum
here it is please register
References
- SMAC W. Krauth Statistical Mechanics: Algorithms and Computations (Oxford: Oxford University Press) (2006)
- Other references are specified in each lectures