Difference between revisions of "NUMPHYsandML"

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(Schedule)
(Schedule)
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Lecture 1: Introduction to Monte Carlo
 
Lecture 1: Introduction to Monte Carlo
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Homework 1: Page Rank & Errors
 
Homework 1: Page Rank & Errors
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'''Friday, September 25, 2020'''
 
'''Friday, September 25, 2020'''
  
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Lecture 3: Ising model and phase transitions
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Tutorial 3: Ising model and phase transitions
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''' Due: Homework 1'''
  
Lecture 2: Ising model and phase transitions
 
  
Tutorial 2: Sampling non uniform distribution
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'''Friday, October 2, 2020'''
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Lecture 3: Quantum particle
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Tutorial 3: Time evolution (quantum)
  
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Homework 2:
  
'' Due: Homework 1''
 
  
'''Friday, October 2, 2020'''
 
  
 
'''Friday, October 9, 2020'''
 
'''Friday, October 9, 2020'''
  
=== Friday, October 16, 2020 ===
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Lecture 4: Importance sampling
=== Friday, October 23, 2020 ===
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Tutorial 4: Faster than the clock algorithms
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 +
 
 +
 
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''' Friday, October 16, 2020'''
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Lecture 5: Optimization & Dijkstra algorithm
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Tutorial 5: Simulated annealing
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''' Due: Homework 2'''
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'''Friday, October 23, 2020'''
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'''QCM: 2 hours, 20 questions for 20 points'''
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Lecture 5: more on ptimization
  
 
== References ==
 
== References ==
 
* [http://www.lps.ens.fr/~krauth/index.php/SMAC SMAC W. Krauth Statistical Mechanics: Algorithms and Computations (Oxford: Oxford University Press) (2006)]
 
* [http://www.lps.ens.fr/~krauth/index.php/SMAC SMAC W. Krauth Statistical Mechanics: Algorithms and Computations (Oxford: Oxford University Press) (2006)]
 
* Other references are specified in each lectures
 
* Other references are specified in each lectures

Revision as of 15:04, 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

= 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) [?? to be decided]


Schedule

Friday, September 4, 2020

Lecture 1: Introduction to Monte Carlo


Friday, September 11, 2020

Tutorial 1: Markov matrix & thumb rule

Homework 1: Page Rank & Errors


Friday, September 18, 2020

Lecture 2: Error evaluation

Tutorial 2: Sampling non uniform distribution


Friday, September 25, 2020

Lecture 3: Ising model and phase transitions

Tutorial 3: Ising model and phase transitions

Due: Homework 1


Friday, October 2, 2020

Lecture 3: Quantum particle

Tutorial 3: Time evolution (quantum)

Homework 2:


Friday, October 9, 2020

Lecture 4: Importance sampling

Tutorial 4: Faster than the clock algorithms


Friday, October 16, 2020

Lecture 5: Optimization & Dijkstra algorithm

Tutorial 5: Simulated annealing

Due: Homework 2


Friday, October 23, 2020

QCM: 2 hours, 20 questions for 20 points

Lecture 5: more on ptimization

References