Data Mining II (FSS 2024)
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, students enrolled in the course will participate in a larger data mining competition (details to be announced). In addition to the submission of an entry to the competition, 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
Lecture:
- Tuesday, 13.45 – 15.15, A5, 6, C013 (starts on February 13th!)
We'll have two alternatives for the exercise:
- Exercise: Monday, 12.00 – 13.30, A5, 6, C013
- Exercise: Monday, 13.45 – 15.15, A5, 6, C013
Both exercises are equivalent, you are supposed to attend one.
Instructors
Lecture Slides
Lecture slides will be made available here as the course progresses.
- 13.02.: Organization (PDF, 4 MB), Data Preprocessing (PDF, 5 MB)
- 27.02.: Ensembles (PDF, 4 MB)
- 05.03.: Time Series (PDF, 4 MB)
- 18.03.: Neural Networks and Deep Learning (PDF, 38 MB)
- 08.04.: Anomaly Detection (PDF, 8 MB) (and challenge introduction)
- 23.04.: Hyperparameter Optimization (PDF, 3 MB)
- 07.05.: Model Validation (PDF, 4 MB)
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, places will be allocated automatically by Portal2.
Outline
Week Exercise (Monday) Lecture (Tuesday) 12.2. -- Introduction & Data Preprocessing 19.2. Introduction & Data Preprocessing -- 26.2. -- Ensembles 4.3. Ensembles Time Series 11.3. Time Series -- 18.3. --
Neural Networks & Deep Learning 25.3. --
Easter Break 1.4. --
Easter Break 8.4. Neural Networks & Deep Learning Anomaly Detection & Challenge Kick-off 15.4. Anomaly Detection Challenge Session 22.4. -- Hyperparameter Tuning 29.4. Hyperparameter Tuning Challenge Session 6.5. -- Model Verification 13.5. Model Verification Challenge Session 20.5. --
Challenge Session Deadlines for the challenge (see here):
- May 24th: submission of predictions
- May 26th: submission of reports
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).