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Tomorrow Florent cannot really give the talk in direct live ... but never fear: | |||
-he can make a short Q/A tomorrow at, say, 15h or 15H30 | |||
-he registered the whole lecture in video, and put it here: | |||
[https://www.dropbox.com/s/1yvmqbb5bb67w8n/video_lec4.mov?dl=0 Lecture 11] | |||
[https://www.dropbox.com/s/dl31z2306y9salr/ML-lec4.pdf?dl=0 The notes] | |||
[https://colab.research.google.com/drive/1OfxV5oL-9CVOxuhKgUF8AsboB89fm2xQ?usp=sharing Tutorial 11] Deep neural networks | [https://colab.research.google.com/drive/1OfxV5oL-9CVOxuhKgUF8AsboB89fm2xQ?usp=sharing Tutorial 11] Deep neural networks | ||
[https://colab.research.google.com/drive/1Zblg4v9RE-zcIgIHI3It9Kw8EmzcvwjR?usp=sharing problems] | [https://colab.research.google.com/drive/1Zblg4v9RE-zcIgIHI3It9Kw8EmzcvwjR?usp=sharing problems] | ||
Revision as of 20:24, 26 November 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.
Lectures on machine learning will be done remotely. You will be able to access them on https://epfl.zoom.us/my/krzakalaflorent Tutorials will take place as usual on the gotomeeting.
The Team
- Alberto Rosso (Numerical Physics)
- Florent Krzakala (Machine Learning)
- Marko Medenjak (Tutorials)
Where and When
- Lectures on Fridays: 14:00-16:00
- Tutorials on Fridays: 16:00-18:00
- ENS, 24 rue Lhomond, room L367 (third 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
Homeworks (10 points each) + 1 MCQ (30 points)
Here you can find a trial of the MCQ 10 Questions about numerical physics
you can find a short presentation video of the MCQ on the Dropbox by A. Rosso
Schedule
Project in Markox Chain Monte Carlo Manon Michel's Project
Friday, September 4, 2020
- Lecture 1 Introduction to Monte Carlo
- Tutorial 1 Markov Matrix
Friday, September 11, 2020
- Lecture 2 Basic Sampling
- Tutorial 2 Markov matrix problems
Friday, September 18, 2020
- Lecture 3: Errors and Precision
- Tutorial 3 Thumb rule problems
Homework: Download
Friday, September 25, 2020
- Lecture 4: Ising model and phase transitions
- Tutorial 4: Ising model and phase transitions problems
Friday, October 2, 2020
Lecture 5: Quantum particle
Tutorial 5: Time evolution (quantum) problems
Homework 2: Download
Friday, October 9, 2020
Lecture 6: Importance sampling
Tutorial 6: Faster than the clock algorithms problems
Friday, October 16, 2020
GoToMeeting link [1] (Room 1 M2 ICFP)
Lecture 7: Optimization & Dijkstra algorithm
Tutorial 7: Simulated annealing problems
Friday, October 23, 2020
Lecture 8: Maximum Likelyhood estimation: follow the link [2]
Tutorial 8: Maximum Likelyhood estimation problems
Due: Homework 2 (send it to Marko)
Friday, November 06, 2020
Lecture 9: Restricted Boltzmann machines
Tutorial 9: Restricted Boltzmann machines problems
Friday, November 13, 2020
Tutorial 10: k-NN and regression problems
Homework 3 Due by December 4, 2020 Homework Data
Friday, November 27, 2020
Tomorrow Florent cannot really give the talk in direct live ... but never fear: -he can make a short Q/A tomorrow at, say, 15h or 15H30 -he registered the whole lecture in video, and put it here:
Tutorial 11 Deep neural networks problems
Friday, December 4, 2020
Modern neural networks
Due: Homework 3 (send it to me (Marko))
Friday, December 11, 2020
Multiple Choice Questions: the final test
References
- SMAC W. Krauth Statistical Mechanics: Algorithms and Computations (Oxford: Oxford University Press) (2006)
- Other references are specified in each lectures