After successful completion of the course, students are able to...

• understand and summarise research papers;
• derive the needed information to (re)implement learning algorithms;
• develop implementations/applications of learning algorithms;
• apply them to data sets and/or in applications;
• empirically evaluate (and experiment with) machine learning algorithms;
• identify appropriate hyperparameters for the algorithms;
• compare different learning algorithms to analyse their strengths and weaknesses.

The goal of this project is to understand, (re)implement learning algorithms, and apply them. Selected algorithms should be evaluated with a variety of  hyperparameters, data sets, and/or applications. You can find some suggestions for projects on our homepage. We also look forward to hearing your own creative and concrete project ideas (check criteria on homepage).

The main part of this project consists of implementing existing learning algorithms. During the project, the students will continuously give online progress presentations and receive formative feedback. Finally, the students will give a full online  presentation and submit a report summing up the outcome.

3ects -> 75h
8h literature search and proposal writing
12h preparing and attending presentations and project meetings
42h data preparation, design, implementation, documentation, and evaluation
13h writing the project report

The final grade is derived from the quality of

• the implementation (runnability, scalability, runtime, documentation),
• the online presentations, and
• the submitted written report.