Data Mining II
Building on the Data Mining fundamentals course, this course deepens the theory and practice of advanced data mining topics, such as:
- Data Preprocessing
- Regression and Forecasting
- Dimensionality Reduction
- Anomaly Detection
- Time Series Analysis
- Parameter Tuning
- Ensemble Learning
- Online Learning
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 tools on realistic data sets. The team projects take place in the last third of the term. Within the projects, 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.
Time and Location
- Lecture: Monday, 10:15 - 11:45, B6 A104
- Exercise: Tuesday, 13:45 - 15:15, B6 A104
- 50 % written exam
- 50 % project work
Slides and Excercises
- Lecture: Organization (Slides)
- Lecture: Data Preprocessing (Slides)
- Exercise: Data Preprocessing (Tasks)
- Lecture: Regression (Slides)
- Exercise: Regression (Tasks | Dataset)
- Lecture: Online Learning (Slides)
- Exercise: Online Learning (Tasks | Dataset)
- Lecture: Anomaly Detection (Slides)
- Exercise: Anomaly Detection (Tasks | Dataset)
- Lecture: Ensembles (Slides)
- Exercise: Ensembles (Tasks)
- Lecture: Time Series Analysis (Slides)
- Exercise: Time Series Analysis (Tasks | Dataset)
- Lecture: Optimization and Parameter Tuning (Slides)
- Exercise: DMC 2014 (Tasks)
- Exercise: DMC 2015 Kick-off (Slides)
Participation FSS 2014
- The course is open to students of the Master Business Informatics and Lehramt Informatik.
- The course is restricted to 30 participants.
- Registration is done via the ILIAS group
- Registration will be opened Wednesday, February 4th, 8:00 am
- Allocation of places is done by FCFS (limit 30 students)
|Week||Session 1||Session 2||Important Dates|
|9.2.||Lecture: Preprocessing||Exercise: Preprocessing|
|16.2.||Lecture: Regression||Exercise: Regression|
|23.2.||Lecture: Online Learning||Exercise: Online Learning|
|2.3.||Lecture: Anomaly Detection||Exercise: Anomaly Detection|
|9.3.||Lecture: Ensembles||Exercise: Ensembles||Tuesday 10.3. DMC Registration Opens|
|16.3.||Lecture: Time Series||Exercise: Time Series|
|23.3.||Lecture: Parameter Tuning||Exercise: Parameter Tuning|
|6.4.||Easter||Break||Tuesday 7.4. DMC Task Publication|
|13.4.||Task discussion, team building||Work on DMC tasks|
|20.4.||Intermediate Presentation||Work on DMC tasks|
|27.4.||Work on DMC tasks||Intermediate Presentation|
|4.5.||Work on DMC tasks||Intermediate Presentation|
|11.5.||Work on DMC tasks||Intermediate Presentation|
|18.5.||Work on Final Submission and Presentation||Work on Final Submission and Presentation||Tuesday 19.5. DMC Task Submission Deadline|
|25.5.||-||Final presentation||Monday 25.5. Final Report Submission Deadline|
- Pang-Ning Tan, Michael Steinbach, Vipin Kumar: Introduction to Data Mining, Pearson.
- Ian H. Witten, Eibe Frank, Mark A. Hall: Data Mining: Practical Machine Learning Tools and Techniques, 3rd Edition, Morgan Kaufmann.
- Bing Liu: Web Data Mining, 2nd Edition, Springer.
Further literature on specific topics will be announced in the lecture.
- In this year, we will be able to work with the newester version of RapidMiner. Licence key handling will be discussed within the first sessions of this course.
- Video recordings of the Data Mining II lectures are available here.