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- Here you find the MCQ proposed last year
The Solution 
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.
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.
- Preliminary version of the book: Information, Inference, Networks: From statistical physics to big biological data
- 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
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.
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.
- Homework 1: 5 points
- Homework 2: 5 points
- Multiple Choice Questions in November: 10 points
Data Driven Physics:
- Final exam in January: 20 points
Friday, September 3, 2021
- Lecture 1 Introduction to Monte Carlo
- Tutorial 1 Markov Matrix
Friday, September 10, 2021
- Homework 1 (deadline October 1)
Friday, September 17, 2021
- Lecture 2 Basic Sampling
- Lecture 3: Errors and Precision
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!
- Homework 2 (deadline October 22)
Friday, October 8, 2021
- Lecture 6: Introduction to Bayesian inference
- Tutorial 6: Bayesian inference and single-particle tracking. Questions. Data. Starting Notebook. Google colab version. Solutions . Notebook 
Friday, October 15, 2021
- Lecture 7: Importance sampling
Friday, October 22, 2021
- Lecture 8: Asymptotic inference and information. Extra material: Proof of Cramer-Rao bound 
- 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 
- Tutorial 9: Replay of the neuronal activity during sleep after a task.Data . Initial Notebook. Biblio.
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):
Friday, November 26, 2021
- Lecture 10: Priors, regularisation, sparsity
- Tutorial 10: Bayesian Inference and Priors for the analysis of gravitational waves. Starting notebook on artificial data. BiblioNotebook on real data
Friday, December 3, 2021
- Lecture 11: Probabilistic graphical models
- Tutorial 11:Analysis of protein sequence data to infer protein structure Starting notebook and data BiblioSolutions Final notebook
Friday, December 10, 2021
- Lecture 12: Hidden Markov Models. Extra material: Pedagogical introduction to Kalman filters 
- Tutorial 12:
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 
- On-line version of the book 
- Examination repository