Photo credit: Anna Logue
Data Mining II (FSS 2023)
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.
Time and Location
Lecture:
- Tuesday, 13.45 – 15.15, SN 163 (starts on February 21st)
We'll have two alternatives for the exercise:
- Exercise: Monday, 12.00 – 13.30, A5, 6, C012
- Exercise: Monday, 13.45 – 15.15, A5, 6, C012
The exercises start on February 27th.
All exercises are equivalent, you are supposed to attend one.
Instructors
Lecture Slides
Lecture slides:
- 21.02.2023: Organization, Data Preprocessing
- 28.02.2023: Ensemble Methods
- 07.03.2023: Time Series Analysis
- 10.03.2023: Neural Networks and Deep Learning
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 Lecture Exercise 14.2. -- -- 21.2. Introduction & Data Preprocessing -- 28.2. Ensembles Introduction & Data Preprocessing 7.3. Time Series Ensembles 14.3. Neural Networks & Deep Learning Time Series 21.3. New: KDD Cup Kick Off Neural Networks & Deep Learning
28.3. Hyperparameter Tuning --
4.4. Easter Break Easter Break
11.4. Easter Break Easter Break 18.4. KDD Cup
Hyperparameter Tuning 25.4. Model Verification --
2.5. KDD Cup Model Verification 9.5. Anomaly Detection -- 16.5. KDD Cup Anomaly Detection 23.5. KDD Cup -- KDD Cup Timeline: see here
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).