Photo credit: Anna Logue
Data Mining (HWS2023)
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
- Association Analysis
- 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.
Exam Review
The exam review for FSS2023 will take place on Wednesday, 13 September 2023, starting from 11:00.
You have to register for the exam review by writing a mail to Alexander Brinkmann until Tuesday, 6 September 2023.
Instructors
Time and Location
- Lecture: Wednesday, 10.15 – 11.45, Room SN 169 (Castle Schneckenhof Nord)
- Exercises: Students should attend one of the three exercise groups. The contents are identical.
- Thursday, 12.00 – 13.30, Room B6 D0.07 (with Nico)
- Thursday, 13.45 – 15.15, Room B6 D0.07 (with Ralph)
- Thursday, 15.30 – 17.00, Room B6 D0.07 (with Andreea)
Grading
- 75 % written exam (we offer only a single exam and no re-take as the course is offered every semester)
- 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 FSS 2023 will be preferred, equally ranked students will be drawn randomly.
- You don't have to register for the Exercise.
Outline
Week Wednesday Thursday 04.09.2023 Lecture: Introduction
Exercise: Introduction to Python / Preprocessing & Visualization
11.09.2023 Lecture: Clustering Exercise: Clustering 18.09.2023 Lecture: Classification I Exercise: Classification I 25.09.2023 Kick Off Team Project -- 02.10.2023 Lecture: Classification II Exercise: Classification II 09.10.2023 Lecture: Regression Exercise: Regression 16.10.2023 Lecture: Text Mining Exercise: Text Mining 23.10.2023 Lecture: Association Analysis Exercise: Association Analysis 30.10.2023 Team Project Feedback --
06.11.2023 Team Project Feedback --
13.11.2023 Team Project Feedback --
20.11.2023 Team Project Feedback --
27.11.2023 Results Presentation -- 04.12.2023 Results Presentation -- Important project dates:
- Project Proposal due: Sunday, October 8th, 23:59
- Project Report due: Friday, December 8th, 23:59
Lecture Videos and Slides
Lecture slides:
- 06.09.: Organization & Introduction
- 13.09.: Cluster Analysis
- 20.09.: Classification I
Exercise material (e.g., slides, solutions) can be found in the ILIAS group of the course.
Literature
Pang-Ning Tan, Michael Steinbach, Anuj Karpatne, Vipin Kumar: Introduction to Data Mining, 2nd Global Edition, Pearson.
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