Phelma Formation 2022

Computational statistics and statistical learning - VPMDCSS1

  • Volumes horaires

    • CM 5.0
    • Projet 0
    • TD 13.0
    • Stage 0
    • TP 12.0

    Crédits ECTS

    Crédits ECTS 6.0


This course aims at giving some basis of statistical inference as well as some elementary ML tools, with illustrations borrowed from Physics and Materials science. Links to other lecture of the cursus will be given such as initiation to experiment design and research. This course is to serve as an introductory resource for those looking to transition to more industry-oriented projects as well as academic research as data scientists or researchers. A familiarity with ML is often a prerequisite for many of the most exciting employment opportunities (academic and industry).

Contact Noel JAKSE


  • Statistical Inference
  • Machine Learning tools: classification and regression (Supervised) as well as (clustering)
  • Lab practices and exercises in an important aspect to acquire these tools


Students shouls have Physics background in mind and more particularly in statistical physics. It assumes a basic level of familiarity with mathematical techniques such as linear algebra, multivariate
calculus, variational methods, probability theory, and Monte-Carlo methods. It also assumes a familiarity with basic computer programming and algorithmic design as well as Python programming language.

Contrôle des connaissances

Semestre 7 - L'examen existe uniquement en anglais 

Contrôle continu : QCM (non rattrapable)
Rapports des TP effectués sur PC (non rattrapable)
Examen final (DS) sur papier et sur PC (Documents autorisés/ calculatrice autorisée)

Session 1 : 20% MQC + 30% Lab. reports + 50% Written Exam
Session 2 : 20% MQC (Session 1) + 30% Lab. reports (Session 1) + 50% Retake exam (Session 2)

Informations complémentaires

Semestre 7 - Le cours est donné uniquement en anglais EN

Cursus ingénieur->Masters->Semestre 7


T. Hastie, R. Tibshirani, J. Friedman, \textit{The Elements of Statistical Learning: Data Mining, Inference, and Prediction} (Springer 2009).

B. Efron, T. Hastie, \textit{Computer Age Statistical Inference: Algorithms, Evidence, and Data Science} (Cambridge University Press 2016).

G. Carleo, I. Cirac, K. Cranmer, L. Daudet, M. Schuld, N. Tishby, L. Vogt-Maranto, and L. Zdeborová, Rev. Mod. Phys. 91, 45002 (2019).