Descriptif

• Goal:
  • The goal of the course is to enrich the participants with practical tools to be able to process and analyse data of functional nature, typically arising in climate research (for example, temperature curves, wind speed, and other time-series data). During the semester we will learn how to denoise functions (smoothing), align sample curves (time warping; shift registration, landmark registration, continuous registration), discover dominant patterns present in the data (dimensionality reduction), represent unequally spaced observations (functional representation), and visualize the obtained results. The course will also provide the possibility to work on hourly, atmospheric observations provided by the SIRTA-ReOBS project. The methods will be implemented in Matlab.
  • What you will learn: denoise experimental data, find recurrent patterns within the data, work with time series, and see applications of machine learning to process experimental data.
• Prerequisites:
  • The course requires a basic knowledge of probability theory (random variable, density function, mean, variance) at the level of 'A First Course in Probability by Sheldon Ross', calculus (function, derivative, integrals) at the level of 'Calculus by James Stewart', linear algebra (vector, matrix) at the level of 'Introduction to Linear Algebra by Serge Lang'.

Langue du cours : Anglais