Kursdetails

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.

Sprache

Schlüsselwörter

Zielgruppe

Kursart

Bildungsniveau

ECTS-Punkte

Anzahl der Lernenden

Bereitstellungsmodus

Blended

Planungsniveau

Einfach

Status

In Planung

Öffentlicher Zugang zum Kurs

Locked

AI usage levels

Lernziele

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.

Verstehen

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

Verstehen

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

Bewerten

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

Anwenden

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