After successful completion of the course, students are able to implement complex media analysis systems comprising of signal processing and machine learning. Implementation includes design, programming and evaluation based on ground truth.
IMPORTANT: Successful registration for the course requires successful completion of the Registration Assessment Test in the e-learning course! Please register via "Self-Enrollment" in the linked Tuwel course, read the assignment description and take the test before the deadline.
Classical (multimedia) data analysis with signal processing and machine learning (without Deep Learning) using audiovisual media as an example:
- 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
The goal is for students to learn the classical methods of information retrieval that are still relevant today when, for example, training data is not sufficiently available or limitations in processing capacity arise (e.g., edge computing).
Similarity Modeling 2 deals with more complex methods than Similarity Modeling 1 and builds on this course. However, the organization is the same in both courses.