Organization (WiSe 25/26)
- Course
- 3+2 hours weekly (equals 6 ECTS)
- Lecture:
- Prof. Dr. Thomas Seidl
- Assistant:
- Dr. Udo Schlegel, Walid Durani
- Audience:
- Master students in the programs of the Institute for Informatics
- Course Material:
- DeepClustering@Moodle
- Prior Knowledge:
- The course expects participants to have basic skills in machine learning and data mining.
- Course Language:
- English
Content
What is Deep Clustering, and why does it matter?
Deep Clustering sits at the intersection of unsupervised learning and deep representation learning. As data continues to scale in size and complexity, traditional clustering methods like k-means or hierarchical clustering fall short. Deep clustering methods integrate neural networks with clustering objectives, allowing models to simultaneously learn rich feature representations and group structures—enabling breakthroughs in computer vision, NLP, bioinformatics, and beyond.