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 Methods
- Deep 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.
Like in the previous years, participants will take part in the annual Data Mining Cup (DMC), an international student competition in data mining, as part of the project work. In addition to the DMC submission, the approaches and results of the project have to be compiled into a written project report, and presented in a plenary session.
Time and Location
- Exercise: Monday, 10.15 - 11.45, A 5, 6, C012
- Lecture: Tuesday, 13.45 - 15.15, B6 23-25, A 104
- 60 % written exam
- 40 % project work
Slides and Excercises
- 14.02.: Organization, Data Preprocessing
- 20.02.: Exercise Data Preprocessing
- 21.02. Regression
- 27.02.: Exercise Regression
- 28.02.: Anomaly Detection
- 06.03.: Exercise Anomaly Detection (Task | Data)
- 07.03.: Ensembles
- 13.03.: Exercise Ensembles
- 14.03.: Time Series
- 20.03.: Exercise Time Series (Task | Data)
- 21.03.: Neural Networks & Deep Learning
- 28.03. Parameter Tuning
Participation FSS 2016
- The course is open to students of the Master Business Informatics, Master Data Science, and Lehramt Informatik.
- The course is restricted to 30 participants.
- Registration is done via the ILIAS group
- Registration will be opened Friday, February 10th, 9:00 am, using this link
- Allocation of places is done by FCFS (limit 30 students)
|20.2.||Exercise: Preprocesing||Lecture: Regression|
|27.2.||Exercise: Regression||Lecture: Anomaly Detection|
|6.3.||Exercise: Anomaly Detection||Lecture: Ensembles|
|13.3.||Exercise: Ensembles||Lecture: Time Series|
|20.3.||Exercise: Time Series||Lecture: Neural Networks|
|27.3.||Exercise: Neural Networks||Lecture: Parameter Tuning|
|3.4.||Exercise: Parameter Tuning||-|
|24.4.||Work on DMC Task||Intermediate Presentation|
|8.5.||Work on DMC Task||Intermediate Presentation|
|15.5.||Work on DMC Task||Consultation for final submission|
- 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.
- We will use the most recent 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 (accessible from within the university network or VPN).