Lectures on Data Science:
Unsupervised Learning
Tuesday, 20.05.2025
ยท 9 a.m. - 5 p.m.
On-site
The course aims to teach you the basic knowledge of Unsupervised Learning with several hands-on sessions.
Content:
- Introduction (course structure, classification data mining, practical procedure)
- Lecture Clustering (Partitioning and Density-Based Models and Algorithms, High-Dimensional
- Data)
- Exercise Clustering
- Lecture Outlier Detection (Statistics, Local and Scoring-Based Approaches, High-Dimensional
- Data)
- Exercise Outlier Detection
- Lecture Association Rule Mining and Frequent Pattern Mining
- Exercise Association Rule Mining
- Final round (questions, feedback)
Trainers: Prof. Ira Assent, FZJ
Requirements: Python basics are required.
