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
- Tuesday, 10.15 - 11.45, room A303 in B6, start: 11.02.2014
- Thursdays, 10.15 - 11.45, room A303 in B6, start: 14.02.2014
- 50 % written exam
- 50 % project work
Slides and Excercises
- Lecture 1: Introduction and Data Preprocessing
- Exercise 1: Data Preprocessing
- Lecture 2: Regression
- Exercise 2: Regression (DataSet)
- Lecture 3: Anomaly Detection
- Exercise 3: Anomaly Detection (DataSet1, DataSet2)
- Lecture 4: Ensemble Methods
- Exercise 4: Ensemble Methods
- Lecture 5: Time Series
- Exercise 5: Time Series (DataSet)
- Lecture 6: Online Learning
- Exercise 6: Online Learning (DataSet (download from ILIAS))
- Lecture 7: Parameter Tuning and Optimization
The solutions to the exercises will be provided in ILIAS.
Participation FSS 2014
- The course is open to students of the Master Business Informatics and Lehramt Informatik.
- The course is restricted to 40 participants.
- Registration is done via the ILIAS group
- Registration will be opened Wednesday, February 5th, 8:00 am
- Allocation of places is done by FCFS (limit 40 students)
Exam Review of the FSS 2014 Exam
- Monday, 15.09.2014 2.00-2.30pm, Room: B6, 26 A2.06
Outline (may be subject to slight changes)
|10.2.||Lecture: Preprocessing||Exercise: Preprocessing|
|17.2.||Lecture: Regression||Exercise: Regression|
|24.2.||Lecture: Anomaly Detection||Exercise: Anomaly Detection|
|3.3.||Lecture: Ensembles||Exercise: Ensembles|
|10.3.||Lecture: Time Series||Exercise: Time Series|
|17.3.||Lecture: Online Learning||Exercise: Online Learning|
|24.3.||Lecture: Parameter Tuning||Exercise: Parameter Tuning|
|31.3.||Wednesday: DMC tasks out||Task discussion, team building|
|7.4.||Work on DMC tasks||Intermediate result presentation|
|28.4.||Intermediate result presentation||Work on DMC tasks|
|5.5.||Work on DMC tasks||Intermediate result presentation|
|12.5.||Wednesday: DMC deadline|
|19.5.||Monday: Final report due||Final presentation|
- 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.