392123 Introduction to Data mining (V) (WiSe 2024/2025)
Contents, comment
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The course covers foundational methods of data mining, explorative data analysis, and visualization. The focus is on educational data (so-called 'educational data mining' or 'learning analytics'). Example methods are:
- statistical tests, esp. t-tests and signed rank test
- Principal Component Analysis
- k-Means Clustering
- Item Response Theory
- Bayesian Knowledge Tracing
- Variational Auto Encoders
- Generative Models
The core skills taught are:
- critical, analytical thinking about models and applications of data mining
- how to design models (formalization, following modeling arguments)
- how to implement models (in Python)
- debugging and interpreting models when applied to data
Requirements for participation, required level
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Useful prior knowledge: Neural Networks, Linear Algebra, Probability Theory
Relations to: Information Visualization, Introduction to Machine Learning, Pattern Recognition, Unsupervised Machine Learning, Generative AI
Bibliography
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- Baker, Yacef (2009): The State of Educational Data Mining in 2009: A Review and Future Visions. https://jedm.educationaldatamining.org/index.php/JEDM/article/view/8
- Abdi, Williams (2010): Principal component analysis. https://doi.org/10.1002/wics.101
- Jain (2010): Data clustering: 50 years beyond K-means. https://doi.org/10.1016/j.patrec.2009.09.011
- Baker (2001): The basics of item response theory. https://files.eric.ed.gov/fulltext/ED458219.pdf
- Abdelrahman, Wang, Nunes (2022): Knowledge Tracing: A Survey. https://doi.org/10.1145/3569576
- Paaßen, Dywel, Fleckenstein, Pinkwart (2022): Sparse Factor Autoencoders for Item Response Theory. https://files.eric.ed.gov/fulltext/ED624123.pdf
Teaching staff
Dates ( Calendar view )
| Frequency | Weekday | Time | Format / Place | Period | |
|---|---|---|---|---|---|
| weekly | Mo | 12-14 | U2-233 | 07.10.2024-31.01.2025
not on: 12/23/24 / 12/30/24 |
Subject assignments
| Module | Course | Requirements | |
|---|---|---|---|
| 39-Inf-DM Introduction to Data Mining Grundlagen Datamining | Grundlagen Datamining | Ungraded examination
Graded examination |
Student information |
| 39-Inf-WP-DS Data Science (Basis) Data Science (Basis) | Einführende Vorlesung | Student information | |
| - | Graded examination | Student information | |
| 39-Inf-WP-DS-x Data Science (Focus) Data Science (Schwerpunkt) | Einführende Veranstaltung Seminar o. Vorlesung | Student information | |
| - | Graded examination | Student information | |
| 39-M-Inf-AI-bas Basics of Artificial Intelligence Basics of Artificial Intelligence | Basics of Artificial Intelligence: Lecture | Student information | |
| - | Ungraded examination | Student information |
The binding module descriptions contain further information, including specifications on the "types of assignments" students need to complete. In cases where a module description mentions more than one kind of assignment, the respective member of the teaching staff will decide which task(s) they assign the students.
Students need to achieve 50% points in the exercises, need to present their exercises at least two times in the tutorial, and need to pass a final, written exam
- Moodle Courses
- Moodle Courses
This course has a video conference. Details will be displayed to you as a participant of this course. For an event with participant management, you must also be registered as attending by the teaching staff.
- Address:
- WS2024_392123@ekvv.uni-bielefeld.de
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- Notes:
- Additional notes on the electronic mailing lists
- Email archive
- Number of entries 16
- Open email archive
- Last update basic details/teaching staff:
- Thursday, August 8, 2024
- Last update times:
- Thursday, July 18, 2024
- Last update rooms:
- Thursday, July 18, 2024
- Type(s) / SWS (hours per week per semester)
- lecture (V) / 2
- Language
- This lecture is taught in english
- Department
- Faculty of Technology
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