Computational statistics and statistical learning - VPMDCSS1

Objectif(s)

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).

Contenu(s)

• 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.