After successful completion of the course, students are able to implement simple media analysis systems comprising of signal processing, machine learning and deep learning. Implementation includes design, programming and evaluation based on ground truth.

• Low-level feature extraction from audiovisual media
• Semantic feature modeling
• Similarity modeling and feature classifciation
• Performance evaluation and statistical data analysis
• Examples of applications and advanced topics

Central topic of the course is understanding the importance of convolution operations for digital media description and similarity measurement. The desired understanding is developed interactively in the lecture part and consolidated by a realistic exercise in the practical part of the course.

Pedagogic concept

• Frame of knowledge transfer with lecture block at the beginning an exam at the end of the lecture
• Exploration of lecture contents in a lab project in groups of 2-3 students
• Application of state of the art visualization and seminar methods for enabling student participation during the lecture
• Application of an open forum for knowledge exchange over groups during the lab course

ECTS Breakdown

Description                       ECTS  Hours
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Preparation                       0.04    1.0
Lecture                           0.32    8.0
Preparation of the Group Project  0.04    1.0
Group Project Work                1.88   47.0
Preparation of the Oral Exam      0.70   17.5
Oral Exam                         0.02    0.5
---------------------------------------------
Total                             3.00   75.0

Students are graded based on their performance in the practical part of the course as well as on thei participation in the lecture part.

List of topics and links in the TUWEL forum

Programming in Java and/or Python