• Donzé Laurent

• Donzé Laurent

• Rosset Julien

The course is an introduction to classification methods:

1. Discriminant analysis (Introduction; binary classification; multiclass classification)

2. Splines (Bézier curves and splines; splines smoothers)

3. Generalized additive models and regression trees (Generalized additive models; classification and regression trees; MARS; boosting algorithms)
4. Random forests and transformation forests

The theoretical concepts and the methods are illustrated by practical applications. R is the software used.

Moodle

The course is part of the supplied of Master courses in applied statistics, which is a packet of four half-yearly ones of 4.5 ECTS (one by semester) over two years:

1. Topics in multivariate statistics
2. Introduction to Bayesian statistics
3. Inference, evaluation and selection of models
4. Classification methods

Although they complete each other, they can be chosen separately. Of general interest, the set of courses gives a broad view of problems and applied statistical methods, and of the data science. A server of Jupyter notebooks completes the course.

There is no specific public. Although the courses are primarily conceived for students of the Faculty of Management, Economics and Social Sciences, they can be attended by other students. 

The student will benefit not only of theoretical knowledge but is also skilled in the use of the methods presented during the course.

Only students who will have validated the course could write a Master thesis under the supervision of the professor. The course is taught in French, but the students can talk in German or English. The exam can be written in one of this language.

A script with a listing of references is provided. The student will find on the course's Moodle platform other resources.

• Méthodes de classification
• ASAM - Méthodes de classification
• SP-2025 - Méthodes de classification

Les étudiant-e-s par groupe de deux ou trois effectuent un projet durant le semestre dont les modalités sont définies en début de semestre.

Durée d'examen: 90 minutes. A livres ouverts

Cours à évaluation continue: la désinscription ou le retrait du cours ne sera plus possible après la période d'inscription (cf. calendrier des sessions sur le site de la Faculté).

Durée d'examen: 90 minutes. A livres ouverts

Uniquement comme examen de rattrapage