Course details

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.

Language

Keywords

Target group

Course type

Educational Level

ECTS credits

Number of learners

Mode of delivery

Blended

Level of planning

Simple

Status

In planning

Course public access

Learning outcomes

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.

Understanding

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

Understanding

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

Evaluating

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

Applying

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