Credit: Anna Logue
Data Mining II (FSS 2021)
Building on the Data Mining fundamentals course, this course deepens the theory and practice of advanced data mining topics, such as:
- Data Preprocessing
- Dimensionality Reduction
- Anomaly Detection
- Time Series Analysis and Forecasting
- Parameter Tuning
- Ensemble Methods
- Neural Networks and Deep Learning
- Model Validation
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.
Exam Review
The exam review for the retake exam of FSS2021 will take place on Thursday, 04 November 2021, starting from 08:00. You have to register for the exam review by writing a mail to Bianca Lermer until Thursday, 28 October 2021. We will then allocate a time slot for the review to you.
Time and Location
Until further notice, the lecture and exercise are conducted fully virtually.
Lecture:
- Tuesday, 13.45 – 15.15, WIM-ZOOM-06
We'll have two alternatives for the exercise:
- Exercise: Monday, 10.15 – 11.45, WIM-ZOOM-06
- Exercise: Monday, 12.00 – 13.30, WIM-ZOOM-06
one.
Instructors
Lecture Slides
Participation
- The course is open to students of the Master Business Informatics, Master Data Science, and Lehramt Informatik.
- Registration is done via Portal2.
- In case there are more registration than places (64), places will be allocated automatically by Portal2.
Outline
Note:The lecture starts the lecture in the second week, i.e., on March, 9th. The exercises will then begin on March, 15th.
Date Topic 9.3. Introduction & Data Preprocessing
16.3. Ensembles 23.3. Time Series 30.3. Easter Break 6.4. Easter Break 13.4. Neural Networks & Deep Learning
20.4. DMC Session 1 27.4. Hyperparameter Tuning
4.5. DMC Session 2 11.5. Anomaly Detection
18.5. DMC Session 3 25.5. Model Verification
1.6. DMC Session 4 8.6. DMC Session 5 15.6. DMC Session 6 Data Mining Cup Timeline (see here):
13.04.: Task Publication
18.06.: Internal submission of reports and solutions (prequisite for taking part in the exam)
29.06.: Official submission of solutions
Note: we will be available for consulting and feedback to those who still want to tune their solutions after the exam period. On June 28th, we will select the two solutions to submit to the DMC.
Literature
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.
Software
- We will use Python with a number of different packages (scikit-learn, etc.), which will be announced during the exercises.
- You are invited to work with other tools (RapidMiner, R, etc.) if you like.
Lecture Videos
- Video recordings of the Data Mining II lectures are available here (accessible from within the university network or VPN).