Credit: Anna Logue
Data Mining (HWS 2021)
The course provides an introduction to advanced data analysis techniques as a basis for analyzing business data and providing input for decision support systems. The course will cover the following topics:
- The Data Mining Process
- Data Representation and Preprocessing
- Clustering
- Classification
- Regression
- Graph Mining
- Text Mining
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.
The webpage about the HWS 2020 edition of this course is found in the lecture archive.
Exam Review
- Detailed information about the exam review will be announced after the exam
Instructors
- Dr. Tobias Weller
- Nicolas Heist
- Sven Hertling
- Ralph Peeters
Time and Location
- Lecture: Wednesday, 10.15 – 11.45, Room A 001 (B 6, Bauteil A) + Zoom (Dr. Tobias Weller)
- Exercises:
- Thursday, 12.00 – 13.30, Zoom (Python with Sven Hertling)
- Thursday, 13.45 – 15.15, Zoom (RapidMiner with Nicolas Heist)
- Thursday, 15.30 – 17.00, Zoom (Python with Ralph Peeters)
The Zoom Links for the Lecture and Exercise are available in ILIAS.
Note: The lecture will be offered as a hybrid course. Students can attend the lecture either in the lecture hall in compliance with the current Covid-19 regulations and hygiene policy or via Zoom. The Exercise is offered entirely online via Zoom.
Final exam
- 75 % written exam
- 25 % project work (20% report, 5% presentation)
Registration
- For attending the course, please register for the lecture in Portal 2. The course is limited to 90 participants. There will be no “first come – first serve”. Students in higher semesters and students that have failed the course in FSS2021 will be preferred, equally ranked students will be drawn randomly.
- You don't have to register for the Exercise.
Lecture Videos, Slides and Exercises
Materials will be uploaded in ILIAS.
Outline
| Week | Wednesday | Thursday |
| 08.09.2021 | Lecture: Introduction | Exercise: Introduction to Python / RapidMiner |
| 15.09.2021 | Lecture: Regression | Exercise: Regression |
| 22.09.2021 | Lecture: Classification I – Logistic Regression | Exercise: Classification I |
| 29.09.2021 | Lecture: Tuning ML Models | Exercise: Tuning ML Models |
| 06.10.2021 | Lecture: Classification II – Decision Trees | Exercise: Classification II |
| 13.10.2021 | Lecture: Classification III – Neural Networks | Exercise: Classification III |
| 20.10.2021 | Kick Off Team Project | |
| 27.10.2021 | Lecture: Text Mining | Exercise: Text Mining |
| 03.11.2021 | Lecture: Clustering | Exercise: Clustering |
| 10.11.2021 | Team Project Feedback |
|
| 17.11.2021 | Team Project Feedback |
|
| 24.11.2021 | Lecture: Graph Mining | Exercise: Graph Mining |
| 01.12.2021 | Results Presentation | |
| 08.12.2021 | Exam FAQ |
Final Exam: Monday, 20.12.201 (Duration: 60 Minutes). The location will be announced in time.
Literature
Pang-Ning Tan, Michael Steinbach, Anuj Karpatne, Vipin Kumar: Introduction to Data Mining, 2nd Global Edition, Pearson.
Vijay Kotu, Bala Deshpande: Predictive Analytics and Data Mining: Concepts and Practice with RapidMiner. Morgan Kaufmann.
Aurélien Géron: Hands-On Machine Learning with Scikit-Learn and TensorFlow. O'Reilly.
Software
Videos and Screen Casts
- Video recordings of the Data Mining I lectures and screen casts of the exercises are available here.
Course Evaluations