# Difference between revisions of "NUMPHYsandML"

(→Schedule) |
(→Schedule) |
||

Line 78: | Line 78: | ||

− | * [https://drive.google.com/file/d/1DijhG_856OAuj42WqY1UM47APmMy3A8B/view?usp=sharing Lecture 4]: Ising model and phase transitions | + | * [https://drive.google.com/file/d/1DijhG_856OAuj42WqY1UM47APmMy3A8B/view?usp=sharing Lecture 4]: Ising model and phase transitions |

− | * [https://colab.research.google.com/drive/1nU4E_pFWjSFPNigzy8-LcJpef26IKY6x?usp=sharing Tutorial 4]: Ising model and phase transitions [https://colab.research.google.com/drive/1AEUX3U7hxDR_YYy8ld3knTEVR1KYa9SP?usp=sharing problems] | + | * [https://colab.research.google.com/drive/1nU4E_pFWjSFPNigzy8-LcJpef26IKY6x?usp=sharing Tutorial 4]: Ising model and phase transitions [https://colab.research.google.com/drive/1AEUX3U7hxDR_YYy8ld3knTEVR1KYa9SP?usp=sharing problems] |

Line 88: | Line 88: | ||

'''GoToMeeting link''' [https://global.gotomeeting.com/join/854835733] (Room 1 M2 ICFP) | '''GoToMeeting link''' [https://global.gotomeeting.com/join/854835733] (Room 1 M2 ICFP) | ||

+ | ''' Due: Homework 1''' | ||

+ | [https://drive.google.com/file/d/1JY7PlB00hGpw1814lUyVor37E-um3xQj/view?usp=sharing Lecture 5]: Quantum particle | ||

− | + | Tutorial 5: Time evolution (quantum) | |

− | |||

− | Tutorial | ||

− | |||

− | |||

− | |||

Homework 2: | Homework 2: | ||

Line 103: | Line 100: | ||

'''Friday, October 9, 2020''' | '''Friday, October 9, 2020''' | ||

− | Lecture | + | Lecture 6: Importance sampling |

− | Tutorial | + | Tutorial 6: Faster than the clock algorithms |

Line 111: | Line 108: | ||

''' Friday, October 16, 2020''' | ''' Friday, October 16, 2020''' | ||

− | Lecture | + | Lecture 7: Optimization & Dijkstra algorithm |

− | Tutorial | + | Tutorial 7: Simulated annealing |

''' Due: Homework 2''' | ''' Due: Homework 2''' | ||

Line 121: | Line 118: | ||

'''Friday, October 23, 2020''' | '''Friday, October 23, 2020''' | ||

− | + | ????????????????????????? | |

− | |||

− | |||

== 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 11:21, 27 September 2020

## Contents

# Numerical Physics and Machine Learning

# IMPORTANT

** Starting from Friday (25-09-2020) we are changing lecture&tutorial's room. Our new room is L367 (third floor)**

The advantage of the new location is that L367 is better equipped (the camera, the video-projector...). The disadvantage is that L367 can only accommodate 24 students and we are slightly overbooking your presence, but from the experience of these weeks we think it is OK.

**If you want to come on Friday, we ask you to write your name in the list below:**

If you do not find a place, please let us know. Thank you very much for your cooperation!

## 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 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 (20 points)

## Schedule

**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**

**GoToMeeting link** [1] (Room 1 M2 ICFP)

** Due: Homework 1**

Lecture 5: Quantum particle

Tutorial 5: Time evolution (quantum)

Homework 2:

**Friday, October 9, 2020**

Lecture 6: Importance sampling

Tutorial 6: Faster than the clock algorithms

** Friday, October 16, 2020**

Lecture 7: Optimization & Dijkstra algorithm

Tutorial 7: Simulated annealing

** Due: Homework 2**

**Friday, October 23, 2020**

?????????????????????????

## References

- SMAC W. Krauth Statistical Mechanics: Algorithms and Computations (Oxford: Oxford University Press) (2006)
- Other references are specified in each lectures