Data Mining (FSS 2026)
The course provides an introduction to advanced data analysis techniques as a basis for analyzing business data and providing input for decision support systems. The course will cover the following topics:
- The Data Mining Process
- Data Representation and Preprocessing
- Classification
- Regression
- Clustering
- Association Analysis
The course consists of a lecture together with accompanying practical exercises as well as student team projects. In the exercises the participants will gather initial expertise in applying state of the art data mining libraries on realistic data sets. The team projects take place in the last third of the term. Within the projects, groups of students realize more sophisticated data mining projects of personal choice and report about the results of their projects in the form of a written report as well as an oral presentation.
Instructors
- Prof. Christian Bizer (Lecture)
- Dr. Ralph Peeters (Lecture, Exercise and Project)
- Aaron Steiner (Exercise and Project)
Time and Location
- Lecture: Wednesday, 10:15 – 11:45, Room B243 Building A 5,6 Part A (Start: 11th February 2026)
- Exercises: Students should attend one of the three exercise groups. The contents are identical.
- Thursday, 10:15 – 11:45, A104 Building B6, 26 Part A
- Thursday, 12:00 – 13:30, A104 Building B6, 26 Part A
- Thursday, 13:45 – 15:15, A104 Building B6, 26 Part A
Grading
- 75 % written exam (we offer only a single exam and no re-take as the course is offered every semester)
- 25 % project work (20% report, 5% presentation)
Registration
- For attending the course, please register for the lecture in Portal 2. The course is limited to 90 participants. There will be no “first come – first serve”. Students in higher semesters and students that have failed the course will be preferred, equally ranked students will be drawn randomly.
- You don't have to register for the Exercise.
Outline and Course Materials
| Week | Monday(Offline Lecture) | Online Lecture (see Ilias Course) | Thursday (Exercise) |
| 11.02.2026 | Introduction to Data Mining | Introduction to Python (13:45–15:15) | |
| 18.02.2026 | Classification 1 | Nearest Centroids | Classification 1 |
| 25.02.2026 | Classification 2 | Comparing Classifiers | Classification 2 |
| 04.03.2026 | Regression | Ensembles | Regression |
| 11.03.2026 | Preprocessing + Intro to Student Project | Preprocessing | |
| 18.03.2026 | Clustering and Anomalies | Hierarchical Clustering | Clustering |
| 25.03.2026 | Feedback on project outlines | Time Series | Time Series |
| - Easter Break | |||
| 15.04.2026 | Association Analysis and Subgroup Discovery | Multi Modal Data | Association Analysis |
| 22.04.2026 | Project feedback session | Project Work | |
| 29.04.2026 | Project feedback session | Project Work | |
| 06.05.2026 | Project feedback session | Project Work | |
| 13.05.2026 | Project feedback session | Project Work | |
| 20.05.2026 | Project Presentations | Project Presentations |
Literature
Pang-Ning Tan, Michael Steinbach, Anuj Karpatne, Vipin Kumar: Introduction to Data Mining, 2nd Global Edition, Pearson.
Aurélien Géron: Hands-On Machine Learning with Scikit-Learn and TensorFlow. O'Reilly.
Software
Videos and Screen Casts
- Video recordings of the Data Mining I lectures and screen casts of the exercises are available here.