The AMVA4NewPhysics network and the CERN laboratories, together with MathWorks personnel, organize a short but focused Machine Learning course at CERN on June 7 – 10. The course, which takes place in the afternoons, targets graduate students as well as researchers of all ages, and gives insight in the most advanced techniques for multivariate statistical analysis tools. As spelt in the INDICO web page,

Techniques for statistical learning including variable transformation, variable selection, and classification are reviewed, with emphasis on low-dimensional datasets typical for physics analysis. The reviewed techniques cover principal component analysis (PCA), kernel PCA, multidimensional scaling, classification by decision trees and their ensembles, classification by kernel methods such as support vector machines, and supervised feature selection by sequential algorithms, ensembles of decision trees and nearest neighbors. Application of these techniques to real-world datasets is illustrated in MATLAB. A MATLAB primer is included in the course, and MATLAB trial licenses will be available to participants.

Since Ilya Narsky, the main instructor of the course, is a former particle physicist and a reknowned expert of MVA, there is ample guarantee that the course will be extremely interesting.

Registrations are open at the course’s INDICO page. Please note that due to space limitations, on-site attendance may be limited. For this reason the acceptance of the registrations will be confirmed only after May 23rd for non-network members.