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Revision as of 14:11, 31 August 2020
Numerical Physics and Machine Learning
Course description
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 either to use Anaconda (See Memento Python) or use google colab.
The Colaboratory platform from Google is quite good way to use powerful computer without buying one:
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