CoDaDri2: Difference between revisions

From Wiki Cours
Jump to navigation Jump to search
No edit summary
Line 120: Line 120:


== Schedule Data-driven Physics ==
== Schedule Data-driven Physics ==


'''Friday, October 14, 2022'''
'''Friday, October 14, 2022'''
Line 128: Line 129:


* Send your copy of Homework 2 to numphys.icfp  at  gmail.com  Thanks!
* Send your copy of Homework 2 to numphys.icfp  at  gmail.com  Thanks!


'''Friday, October 21, 2022'''
'''Friday, October 21, 2022'''
Line 142: Line 144:
* Tutorial 9: [https://drive.google.com/file/d/10y7m1mPf5R5zZN6661Eei92IUNhBL_Zu/view?usp=sharing  Replay of the neuronal activity during sleep after a task].[https://drive.google.com/file/d/11fO26XB6Ri3HDVBvtdBkYxcTRLk1iv5F/view?usp=sharing Data ]. [https://drive.google.com/file/d/1-MFN2ERs_eNs4w6XnLjfzsu2F9REluSt/view?usp=sharing Initial Notebook]. [https://drive.google.com/file/d/11ev4Zwn6DPYLwhMsLyp8S0aQFVu77Ou3/view?usp=sharing Biblio].  
* Tutorial 9: [https://drive.google.com/file/d/10y7m1mPf5R5zZN6661Eei92IUNhBL_Zu/view?usp=sharing  Replay of the neuronal activity during sleep after a task].[https://drive.google.com/file/d/11fO26XB6Ri3HDVBvtdBkYxcTRLk1iv5F/view?usp=sharing Data ]. [https://drive.google.com/file/d/1-MFN2ERs_eNs4w6XnLjfzsu2F9REluSt/view?usp=sharing Initial Notebook]. [https://drive.google.com/file/d/11ev4Zwn6DPYLwhMsLyp8S0aQFVu77Ou3/view?usp=sharing Biblio].  
[https://drive.google.com/file/d/11E75QwrJgDKrutGj9t5NJTRkGtuRgoBI/view?usp=sharing Solutions]. [https://drive.google.com/file/d/1-39oZNNB-35zM4V2tLWtubQnZTdMLtIz/view?usp=sharing Notebook]
[https://drive.google.com/file/d/11E75QwrJgDKrutGj9t5NJTRkGtuRgoBI/view?usp=sharing Solutions]. [https://drive.google.com/file/d/1-39oZNNB-35zM4V2tLWtubQnZTdMLtIz/view?usp=sharing Notebook]


'''Friday, November 25, 2022'''
'''Friday, November 25, 2022'''
Line 149: Line 152:
* Tutorial 10: [https://drive.google.com/file/d/14kaGKRm7uciT7uLVMW_Vqzr8ha-V-Ra2/view?usp=sharing Bayesian Inference and Priors for the analysis of gravitational waves]. [https://drive.google.com/file/d/16UTmpwKm-7UMcIbca8IIGsO-aCmcwKuD/view?usp=sharing Starting notebook on artificial data]. [https://drive.google.com/file/d/1CyiDlXs6ez9-7gnyeBU0YRgV7Nt5ibkn/view?usp=sharing Biblio][https://colab.research.google.com/drive/1pFMOawozlYOIpy0jQWkTfoq-tFp3uvhw?usp=sharing#scrollTo=6picRNBipcYN Notebook on real data]  
* Tutorial 10: [https://drive.google.com/file/d/14kaGKRm7uciT7uLVMW_Vqzr8ha-V-Ra2/view?usp=sharing Bayesian Inference and Priors for the analysis of gravitational waves]. [https://drive.google.com/file/d/16UTmpwKm-7UMcIbca8IIGsO-aCmcwKuD/view?usp=sharing Starting notebook on artificial data]. [https://drive.google.com/file/d/1CyiDlXs6ez9-7gnyeBU0YRgV7Nt5ibkn/view?usp=sharing Biblio][https://colab.research.google.com/drive/1pFMOawozlYOIpy0jQWkTfoq-tFp3uvhw?usp=sharing#scrollTo=6picRNBipcYN Notebook on real data]  
[https://drive.google.com/file/d/161ZLuq5s2RpHDJWELFSKXqlf-qwrVrxA/view?usp=sharing Notebook on Artificial data] [https://drive.google.com/file/d/17qhYN8EFpcoCUHOM_juBvEGpu61GgJRx/view?usp=sharing Corrections ]
[https://drive.google.com/file/d/161ZLuq5s2RpHDJWELFSKXqlf-qwrVrxA/view?usp=sharing Notebook on Artificial data] [https://drive.google.com/file/d/17qhYN8EFpcoCUHOM_juBvEGpu61GgJRx/view?usp=sharing Corrections ]


'''Friday, December 2, 2022'''
'''Friday, December 2, 2022'''
Line 155: Line 159:


* Tutorial 11:[https://drive.google.com/file/d/1A5pcJICHkmHZYoa2uoAGSAWp-_ZOaIEf/view?usp=sharing Analysis of protein sequence data to infer protein structure] [https://drive.google.com/file/d/1COKr5pNoBRFwwnj7TWPQSU8mEWKvsLud/view?usp=sharing Starting notebook and data] [https://drive.google.com/file/d/1CQw1PQ6RSS6nuGxhkwOqe7LJHpOPIaVp/view?usp=sharing Biblio][https://drive.google.com/file/d/1CslX27bTp5gyhXV4ciFE8s1zMnBgJ-1O/view?usp=sharing Solutions] [https://drive.google.com/file/d/19X59x3TdIsJaccTZE71gCbZxFbOzlhj4/view?usp=sharing Final notebook]
* Tutorial 11:[https://drive.google.com/file/d/1A5pcJICHkmHZYoa2uoAGSAWp-_ZOaIEf/view?usp=sharing Analysis of protein sequence data to infer protein structure] [https://drive.google.com/file/d/1COKr5pNoBRFwwnj7TWPQSU8mEWKvsLud/view?usp=sharing Starting notebook and data] [https://drive.google.com/file/d/1CQw1PQ6RSS6nuGxhkwOqe7LJHpOPIaVp/view?usp=sharing Biblio][https://drive.google.com/file/d/1CslX27bTp5gyhXV4ciFE8s1zMnBgJ-1O/view?usp=sharing Solutions] [https://drive.google.com/file/d/19X59x3TdIsJaccTZE71gCbZxFbOzlhj4/view?usp=sharing Final notebook]


'''Friday, December 9, 2021'''
'''Friday, December 9, 2021'''
Line 162: Line 167:
* Tutorial 12:  
* Tutorial 12:  
[https://drive.google.com/file/d/1FLbWZRWJ1JILZV41RjwVKR7mOhNMhLV9/view?usp=sharing Hidden Markov Models Hidden  for identification of recombinations in SARS-CoV-2 viral genomes] [https://drive.google.com/file/d/1FCAg0ihMWoAk-_dtt5ORncjVjkVu-m4D/view?usp=sharing Starting Notebook and Data][https://drive.google.com/file/d/1HMREe6ge7_K-lOKC4f2FwL7vnEa1_8Yf/view?usp=sharing Bibliography] [https://drive.google.com/file/d/1FLDBJmNtXogHiO5VfScGiePTCxMFy1KZ/view?usp=sharing Final Notebook] [https://drive.google.com/file/d/1FBQaNJ2RhjwW_VSut2oC7mZYl5F8RWfS/view?usp=sharing Solutions]
[https://drive.google.com/file/d/1FLbWZRWJ1JILZV41RjwVKR7mOhNMhLV9/view?usp=sharing Hidden Markov Models Hidden  for identification of recombinations in SARS-CoV-2 viral genomes] [https://drive.google.com/file/d/1FCAg0ihMWoAk-_dtt5ORncjVjkVu-m4D/view?usp=sharing Starting Notebook and Data][https://drive.google.com/file/d/1HMREe6ge7_K-lOKC4f2FwL7vnEa1_8Yf/view?usp=sharing Bibliography] [https://drive.google.com/file/d/1FLDBJmNtXogHiO5VfScGiePTCxMFy1KZ/view?usp=sharing Final Notebook] [https://drive.google.com/file/d/1FBQaNJ2RhjwW_VSut2oC7mZYl5F8RWfS/view?usp=sharing Solutions]


'''Friday, December 16, 2022'''
'''Friday, December 16, 2022'''

Revision as of 16:51, 1 September 2022


This is the official page for the year 2022-2023 of the Computational and Data-Driven Physics (CoDaDri) course.

Course description

Modern physics is characterized by an increasing complexity of systems under investigation, in domains as diverse as condensed matter, astrophysics, biophysics, etc. Establishing adequate models to describe these systems and being able to make quantitative predictions from those models is extremely challenging. The goal of the course is to provide the tools and concepts necessary to tackle those systems.

We will first cover many algorithms used in many-body problems and complex systems, with special emphasis on Monte Carlo methods, molecular dynamics, and optimization in complex landscapes.

Second, we will provide statistical inference and machine learning tools to harness the growing availability of experimental data to design accurate models of the underlying, complex, strongly non-homogeneous and interacting systems.

Each theoretical lecture will be followed by a tutorial illustrating the concepts with practical applications borrowed from various domains of physics. We will focus on methods and algorithms and physics, not on programming and heavy numerics! You will have to hand in 3 homeworks.


The Team

Where and When

  • Lectures on Fridays: 14:00-16:00
  • Tutorials on Fridays: 16:00-18:00
  • ENS, 29 rue D'Ulm, salle Borel + Djebar

Slack

If you have questions or want to discuss topics related to the lecture, to the exercises or to the homeworks, you can use the Computational and Data Driven Physics Slack. In order to join the Slack use the following invitation link.

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 Colab 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

Computational Physics:

  • Homework 1: 5 points
  • Homework 2: 5 points
  • Multiple Choice Questions in November: 10 points

Data Driven Physics:

  • Homework 3: 5 points
  • Final exam in January: 15 points

Schedule Computational Physics

Friday, September 2, 2022


Friday, September 9, 2022


Friday, September 16, 2022


Friday, September 23, 2022

  • Send your copy of Homework 1 to numphys.icfp at gmail.com Thanks!


Friday, September 30, 2022


Friday, October 7, 2022

  • Test:

The MCQ is composed of 19 questions (one of them counts for two). For each question you have 4 choices: 3 wrong and 1 correct: If you check the correct one you get a point. If you are wrong you loose 1/4 of a point. No answer given: zero points.


Schedule Data-driven Physics

Friday, October 14, 2022

  • Lecture 7: Introduction to Bayesian inference
  • Send your copy of Homework 2 to numphys.icfp at gmail.com Thanks!


Friday, October 21, 2022

  • Lecture 8: Asymptotic inference and information. Extra material: Proof of Cramer-Rao bound [3]


Friday, October 28, 2022

  • Lecture 9: High-dimensional inference and Principal Component Analysis. Extra material: Handwritten notes on the derivation of Marcenko-Pastur spectral density [4]

Solutions. Notebook


Friday, November 25, 2022

  • Lecture 10: Priors, regularisation, sparsity

Notebook on Artificial data Corrections


Friday, December 2, 2022

  • Lecture 11: Probabilistic graphical models


Friday, December 9, 2021

  • Lecture 12: Hidden Markov Models. Extra material: Pedagogical introduction to Kalman filters [5]
  • Tutorial 12:

Hidden Markov Models Hidden for identification of recombinations in SARS-CoV-2 viral genomes Starting Notebook and DataBibliography Final Notebook Solutions


Friday, December 16, 2022

  • Lecture 13: Unsupervised learning and representations


Retrieved from "http://www.lptms.universite-paris-saclay.fr//wiki-cours/index.php?title=CoDaDri2&oldid=1796"