Arbeitsaufwand = 90 h = 3 ECTS

• Präsenzzeit: 15hr
• Selbststudium: 45h
• Scientific Report schreiben: 20h

Präsentation vorbereiten: 10h

Each student will select several related papers in the field of Interactive Learning. The organizers will suggest several papers but the students will be encouraged to indentify and research additional relevant papers during the semester. The students will then prepare a presentation and a basic scientific research paper.

It is highly recommended to take this seminar in combination with the “Interactive Learning” Research Project (Forschungspraktikum), where the students get the chance to deepen their understanding, implement and evaluate their presented work.

We highly recommend to take this seminar in combination with the “Interactive Learning” research project (Forschungspraktikum).

It is highly recommended to attend the “Explainable Artificial Intelligence” lecture in parallel or prior to this seminar.

·         Experience in Machine Learning is recommended, e.g. through prior coursework.

◦  The Computer Science Department offers several great lectures e.g., “Maschinelles Lernen - Grundlagen und Algorithmen” and “Deep Learning ”

·         A good mathematical background will be beneficial

·         Python experience is recommended

·         We might use the PyTorch deep learning library In the exercises. Some prior knowledge in this is helpful but not necessary.

Vortrag zum gewählten Thema am Ende des Semesters und schriftliche Ausarbeitung.

Ein Rücktritt ist innerhalb von zwei Wochen nach Vergabe des Themas möglich.

It is only possible to resign within two weeks after assignment of the topic

KIT-Fakultät für Informatik/1. Informatik Lehrveranstaltungen/1.7 Seminare