CoDaDri

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Breaking news:

  • 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

The Quiz

The Solution [1]

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

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

Friday, September 3, 2021


Friday, September 10, 2021


Friday, September 17, 2021


Friday, September 24, 2021

  • Lecture 4: Ising model and phase transitions


Friday, October 1, 2021

  • Lecture 5: Optimization & Dijkstra algorithm
  • Send your copy of Homework 1 to numphys.icfp at gmail.com Thanks!


Friday, October 8, 2021

  • Lecture 6: Introduction to Bayesian inference


Friday, October 15, 2021


Friday, October 22, 2021

  • Lecture 8: Asymptotic inference and information. Extra material: Proof of Cramer-Rao bound [4]
  • Send your copy of Homework 2 to numphys.icfp at gmail.com Thanks!


Friday, October 29, 2021

  • Lecture 9: High-dimensional inference and Principal Component Analysis. Extra material: Handwritten notes on the derivation of Marcenko-Pastur spectral density [5]

Solutions. Notebook


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):[6]


Friday, November 26, 2021

  • Lecture 10: Priors, regularisation, sparsity

Notebook on Artificial data Corrections



Friday, December 3, 2021

  • Lecture 11: Probabilistic graphical models


Friday, December 10, 2021

  • Lecture 12: Hidden Markov Models. Extra material: Pedagogical introduction to Kalman filters [7]
  • 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 17, 2021

  • Lecture 13: Unsupervised learning and representations


Final examination of the data-driven course (January 7, 2022)

  • Example of exam: On-line Principal Component Analysis [8]
  • On-line version of the book [9]
  • Examination repository [10]