The objective of MAP 573 is to give students a comprehensive introduction to unsupervised analysis (downsizing and clustering) and to acquire solid and practical skills for the exploratory analysis of current data sets, using R software.
The course begins with two sessions presenting the basics of programming with the R language, as well as data manipulation and the tidyverse graphical representation libraries. R’s Python interfacing capabilities are also discussed.
The course then presents the classical methods of dimensionality reduction and clustering in detail (PCA, mixture models, k-means, CAH), with a brief introduction to the many approaches that generalize them (kernel approaches, embedding, etc.).
Finally, the third part of the module proposes to the students a series of data sets to be studied in groups using the methods of the course and their generalizations. These projects will constitute the main part of the evaluation of this module.
Information: From October 30 and for at least a month, all teachings are online via zoom (link in Moodle).