Détails du cours

A course in modern machine learning methods that covers theoretical and practical aspects. NOTE REGARDING THE WORKLOAD: The requirement is for each participant to complete 12 modules, a team project and a final exam. This works out to a workload of 180h. Since the number of modules available in the course is greater than 12, the total number of hours is greater than 180 – but these modules are not all to be taken by the same participants.

Langue

Mots-clés

Groupe cible

Type de cours

Niveau d'éducation

Crédits ECTS

Nombre d'apprenants

Mode de prestation

Hybride

Niveau de planification

Simple

Statut

En planification

Accès public au cours

Verrouillé

Niveaux d'utilisation de l'IA

Acquis d'apprentissage

The participant is able to explain basic concepts from the field of machine learning such as: machine learning, implicit and explicit knowledge representation, local and global generalization, underfitting, overfitting, bias, variance, regularization and more.

Comprendre

The participant understands and is able to explain the principle of fundamental machine learning methods.

Comprendre

The participant is able to assess where and how machine learning methods can be applied.

Évaluer

The participant is able to apply machine learning methods and approaches.

Appliquer

The participant is able to identify machine learning problems and search for corresponding state of the art methods.