CoDaDri: Difference between revisions
Jump to navigation
Jump to search
No edit summary |
No edit summary |
||
| (58 intermediate revisions by 4 users not shown) | |||
| Line 1: | Line 1: | ||
=Breaking news:= | <!-- =Breaking news:= | ||
* Homework 1 has been evaluated and sent to you. If you did not receive it, please contact us. | * Homework 1 has been evaluated and sent to you. If you did not receive it, please contact us. | ||
* Here you find the MCQ proposed last year | * Here you find the MCQ proposed last year | ||
[https://colab.research.google.com/drive/1Ru_-IQ001XEJd9VAn4nvbBNI_3XA9QSK?usp=sharing The Quiz] | [https://colab.research.google.com/drive/1Ru_-IQ001XEJd9VAn4nvbBNI_3XA9QSK?usp=sharing The Quiz] | ||
'''The Solution''' [https://colab.research.google.com/drive/1Faovrt0q5kRVtVsF9afr4iMXww_Wk53_?usp=sharing] --> | |||
= Computational and Data Driven Physics = | = Computational and Data Driven Physics = | ||
| Line 22: | Line 24: | ||
borrowed from various domains of physics. We will focus on methods and algorithms and physics, not | 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. | on programming and heavy numerics! You will have to hand in 3 homeworks. | ||
*[https://drive.google.com/file/d/11w7KI5Pi59VWLX1mqiQ8ZyY8YQHRUeMz/view?usp=sharing Preliminary version of the book: Information, Inference, Networks: From statistical physics to big biological data] | |||
== The Team == | == The Team == | ||
| Line 65: | Line 69: | ||
* Final exam in January: 20 points | * Final exam in January: 20 points | ||
== Schedule == | == Schedule == | ||
'''Friday, September 3, 2021 ''' | <!-- '''Friday, September 3, 2021 ''' | ||
* [https://drive.google.com/file/d/1X9h3lKD0OZLTKtxb7DWPfynY42rRuE7j/view?usp=sharing Lecture 1] Introduction to Monte Carlo | * [https://drive.google.com/file/d/1X9h3lKD0OZLTKtxb7DWPfynY42rRuE7j/view?usp=sharing Lecture 1] Introduction to Monte Carlo | ||
| Line 83: | Line 87: | ||
* [https://colab.research.google.com/drive/1tjqbjAi50C4qOqVRtTxEkTeTb10qpTms#scrollTo=uYpaubGkvogU Tutorial 3] - Thumb rule ([https://colab.research.google.com/drive/1aWVtz4ZGcpIarWiRVAnxrp1t0GDOqZxu#scrollTo=f7ohlMuZvqTr solutions]) | * [https://colab.research.google.com/drive/1tjqbjAi50C4qOqVRtTxEkTeTb10qpTms#scrollTo=uYpaubGkvogU Tutorial 3] - Thumb rule ([https://colab.research.google.com/drive/1aWVtz4ZGcpIarWiRVAnxrp1t0GDOqZxu#scrollTo=f7ohlMuZvqTr solutions]) | ||
* [https://colab.research.google.com/drive/1g7HXFUBQUBF0fhy5h2jYbfIAVBTqvb7-#scrollTo=U9LFX8OFhHOG Homework 1] (deadline October 1) | * [https://colab.research.google.com/drive/1g7HXFUBQUBF0fhy5h2jYbfIAVBTqvb7-#scrollTo=U9LFX8OFhHOG Homework 1] (deadline October 1) --> | ||
<!-- | <!-- | ||
| Line 90: | Line 94: | ||
--> | --> | ||
'''Friday, September 17, 2021''' | |||
<!-- '''Friday, September 17, 2021''' | |||
* [https://drive.google.com/file/d/15wrgivn6FSnuBUnMwjjhadmD-g1fkS7T/view?usp=sharing Lecture 2] Basic Sampling | * [https://drive.google.com/file/d/15wrgivn6FSnuBUnMwjjhadmD-g1fkS7T/view?usp=sharing Lecture 2] Basic Sampling | ||
| Line 101: | Line 106: | ||
* [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/1sRUEs768Z6PKdJ3k7MixYeFGlufgjFrG Tutorial 4]: Ising model and phase transitions ([https://colab.research.google.com/drive/1KAQRog_YlAjKZ_hL1FMYurWdPBLNUocI solutions]) | * [https://colab.research.google.com/drive/1sRUEs768Z6PKdJ3k7MixYeFGlufgjFrG Tutorial 4]: Ising model and phase transitions ([https://colab.research.google.com/drive/1KAQRog_YlAjKZ_hL1FMYurWdPBLNUocI solutions]) --> | ||
<!-- | <!-- | ||
| Line 108: | Line 113: | ||
'''Friday, October 1, 2021''' | |||
<!--'''Friday, October 1, 2021''' | |||
* [https://colab.research.google.com/drive/1PTya42ZS2kU87A-BxQFFIUDTs_k47men?usp=sharing Lecture 5]: Optimization & Dijkstra algorithm | * [https://colab.research.google.com/drive/1PTya42ZS2kU87A-BxQFFIUDTs_k47men?usp=sharing Lecture 5]: Optimization & Dijkstra algorithm | ||
* [https://colab.research.google.com/drive/1ieT5BlfJsOehsa8r-LHGsFsn1dwYnawT Tutorial 5]: Simulated annealing ([https://colab.research.google.com/drive/1JNVl42KNASZiwTtqYo71z_vggjbGfKul solutions]) | * [https://colab.research.google.com/drive/1ieT5BlfJsOehsa8r-LHGsFsn1dwYnawT Tutorial 5]: Simulated annealing ([https://colab.research.google.com/drive/1JNVl42KNASZiwTtqYo71z_vggjbGfKul solutions]) --> | ||
<!-- | <!-- | ||
* [https://colab.research.google.com/drive/1p0ooWAXF9KNh-FztMHmVoA9yBzYOM8PC?usp=sharing Tutorial 5]: Simulated annealing [https://colab.research.google.com/drive/1txJGpzreHurWux7ev6sDsX8TjzpeQhZZ?usp=sharing problems] | * [https://colab.research.google.com/drive/1p0ooWAXF9KNh-FztMHmVoA9yBzYOM8PC?usp=sharing Tutorial 5]: Simulated annealing [https://colab.research.google.com/drive/1txJGpzreHurWux7ev6sDsX8TjzpeQhZZ?usp=sharing problems] | ||
--> | --> | ||
* Send your copy of Homework 1 to numphys.icfp at gmail.com Thanks! | |||
<!-- * Send your copy of Homework 1 to numphys.icfp at gmail.com Thanks! | |||
* [https://colab.research.google.com/drive/1EPgj3la7vxDIqSQYNlOFz4rR0vHXAeae Homework 2] (deadline October 22) | * [https://colab.research.google.com/drive/1EPgj3la7vxDIqSQYNlOFz4rR0vHXAeae Homework 2] (deadline October 22) | ||
| Line 133: | Line 140: | ||
* [https://drive.google.com/file/d/1eY5cuNcJqYw7df0PBkxq03tEnqzTOC-5/view?usp=sharing Lecture 7]: Importance sampling | * [https://drive.google.com/file/d/1eY5cuNcJqYw7df0PBkxq03tEnqzTOC-5/view?usp=sharing Lecture 7]: Importance sampling | ||
* [https://colab.research.google.com/drive/1iaOGxdVgk--kWUFcMDHs0zvPhsOyhB_c Tutorial 7]: Faster than the clock algorithms ([https://colab.research.google.com/drive/1bHPnJpBd5ExqKF4G5BuV_FHPE4r0_AY2#scrollTo=fQnz_gOeq40e solutions]) | * [https://colab.research.google.com/drive/1iaOGxdVgk--kWUFcMDHs0zvPhsOyhB_c Tutorial 7]: Faster than the clock algorithms ([https://colab.research.google.com/drive/1bHPnJpBd5ExqKF4G5BuV_FHPE4r0_AY2#scrollTo=fQnz_gOeq40e solutions]) --> | ||
<!-- | <!-- | ||
| Line 139: | Line 146: | ||
--> | --> | ||
'''Friday, October 22, 2021''' | |||
<!-- '''Friday, October 22, 2021''' | |||
* Lecture 8: Asymptotic inference and information. Extra material: Proof of Cramer-Rao bound [https://drive.google.com/file/d/10Ph_iP6AIQ3ps9v3FwRBO9j6-qV7oKV9/view?usp=sharing] | * Lecture 8: Asymptotic inference and information. Extra material: Proof of Cramer-Rao bound [https://drive.google.com/file/d/10Ph_iP6AIQ3ps9v3FwRBO9j6-qV7oKV9/view?usp=sharing] | ||
* Tutorial 8: [https://drive.google.com/file/d/12cG4HBvzs9Oe1mbfuEYyRDmTEamW71pR/view?usp=sharing Questions]. [https://drive.google.com/file/d/1AL8sRtnqYUI8NbubfglgLazknaVIUJEo/view?usp=sharing Data ] [https://drive.google.com/file/d/1DTLzXLZUEPzoU2SsDMcAe8GaoYX8E5gH/view?usp=sharing Starting Notebook]. [https://drive.google.com/file/d/1QpMaM5ACmKUlFAyGvRnHvFVtQoEAF7-9/view?usp=sharing Bibliography] [https://drive.google.com/file/d/1p9v1PkJhwS8Ofg4RiC1nsFJAe8Mb_6WF/view?usp=sharing Solutions] [https://drive.google.com/file/d/19erJJ4Gjw77n57VrnqaQPCxIh_-NScYT/view?usp=sharing Notebook ] | * Tutorial 8: [https://drive.google.com/file/d/12cG4HBvzs9Oe1mbfuEYyRDmTEamW71pR/view?usp=sharing Questions]. [https://drive.google.com/file/d/1AL8sRtnqYUI8NbubfglgLazknaVIUJEo/view?usp=sharing Data ] [https://drive.google.com/file/d/1DTLzXLZUEPzoU2SsDMcAe8GaoYX8E5gH/view?usp=sharing Starting Notebook]. [https://drive.google.com/file/d/1QpMaM5ACmKUlFAyGvRnHvFVtQoEAF7-9/view?usp=sharing Bibliography] [https://drive.google.com/file/d/1p9v1PkJhwS8Ofg4RiC1nsFJAe8Mb_6WF/view?usp=sharing Solutions]. [https://drive.google.com/file/d/19erJJ4Gjw77n57VrnqaQPCxIh_-NScYT/view?usp=sharing Notebook]. | ||
* 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! | ||
| Line 152: | Line 160: | ||
* Lecture 9: High-dimensional inference and Principal Component Analysis. Extra material: Handwritten notes on the derivation of Marcenko-Pastur spectral density [https://drive.google.com/file/d/10Yytv9itdWDDHTsM2lQHwQDMbcf-oVnd/view?usp=sharing] | * Lecture 9: High-dimensional inference and Principal Component Analysis. Extra material: Handwritten notes on the derivation of Marcenko-Pastur spectral density [https://drive.google.com/file/d/10Yytv9itdWDDHTsM2lQHwQDMbcf-oVnd/view?usp=sharing] | ||
* 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= | * 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] | |||
| Line 158: | Line 167: | ||
'''Friday, November 12, 2021, 2 pm: The Quiz.''' | '''Friday, November 12, 2021, 2 pm: The Quiz.''' | ||
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. | |||
MCQ Solution (correct answers in bold):[https://drive.google.com/file/d/1WWcqxFzmCJp2ZXC02a70a1o17pGupdyZ/view?usp=sharing] | |||
| Line 166: | Line 177: | ||
* Lecture 10: Priors, regularisation, sparsity | * Lecture 10: Priors, regularisation, sparsity | ||
* Tutorial 10: | * 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 ] | |||
| Line 174: | Line 185: | ||
'''Friday, December 3, 2021''' | '''Friday, December 3, 2021''' | ||
* Lecture 11: | * Lecture 11: Probabilistic graphical models | ||
* Tutorial 11: | * 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 10, 2021''' | '''Friday, December 10, 2021''' | ||
* Lecture 12: | * Lecture 12: Hidden Markov Models. Extra material: Pedagogical introduction to Kalman filters [https://drive.google.com/file/d/0B4HvoS7Zt11LZUxVLTkyVXRQRUE/view?usp=sharing&resourcekey=0-CzsYaRIrPP2sN-UmwfiLHQ] | ||
* 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] | |||
| Line 190: | Line 203: | ||
* Lecture 13: Unsupervised learning and representations | * Lecture 13: Unsupervised learning and representations | ||
* Tutorial 13: | * [https://colab.research.google.com/drive/1p2c35gHMr4_Ptdmwsh3jGJz__9NNIgLw Tutorial 13]: How restricted Boltzmann machines learn ([https://colab.research.google.com/drive/1gnaK_vmTESTV3Ex_A1gqcee6Q58iKyld solutions]) | ||
'''Final examination of the data-driven course (January 7, 2022)''' | |||
* Example of exam: On-line Principal Component Analysis [https://drive.google.com/file/d/1BbRY4b3OCVYAtYH4m6ry19KcA2gkp3pH/view?usp=sharing] | |||
* On-line version of the book [https://drive.google.com/file/d/161YHZA7i2YU-8emy6IPUFhsOXzFRxSRS/view?usp=sharing] | |||
* Examination repository [https://drive.google.com/file/d/19DNkNBed0Ir5a9N048ZFV4_xF0KG3kcy/view?usp=sharing] | |||
--> | |||
Latest revision as of 11:34, 2 September 2022
Computational and Data Driven Physics
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.
Course description
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
- Alberto Rosso (Computational physics)
- Rémi Monasson (Data-driven physics)
- Simona Cocco & David Lacoste & Michel Ferrero (Tutorials)
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 Collaboratory 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:
- Final exam in January: 20 points
== Schedule ==