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School of engineering in Physics, Applied Physics, Electronics & Materials
Science
> Studies
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 JAKSEStudents 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
Semester 7 - The exam is given in english only 
On the Fly Evaluation MCQ (non-recoverable)
Lab reports done on PC (non-recoverable)
Final Examen (DS) paper + PC (Documentation allowed - Certified calculator allowed)
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)
Semester 7 - This course is given in english only
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).
Date of update July 29, 2020
3 Parvis Louis Néel - CS 50257 - 38016 Grenoble Cedex 1
