Course Manual DDML
Data Mining
PDF Course Catalog Deutsche Version: DDML
Version: 1 | Last Change: 12.02.2021 13:46 | Draft: 0 | Status: vom verantwortlichen Dozent freigegeben
| Long name | Data Mining |
|---|---|
| Approving CModule | DML_BaET |
| Responsible |
Prof. Dr. Beate Rhein
Professor Fakultät IME |
| Valid from | summer semester 2023 |
| Level | Bachelor |
| Semester in the year | winter semester |
| Duration | Semester |
| Hours in self-study | 78 |
| ECTS | 5 |
| Professors |
Prof. Dr. Beate Rhein
Professor Fakultät IME |
| Requirements | From Mathematics 1 and 2 the ability to construct mathematical models as well as knowledge of differential calculus and linear algebra is required. |
| Language | German |
| Separate final exam | Yes |
Literature
Final exam
| Details |
Depending on the number of participants: For a small number of participants: combination of exam or oral examination and evaluation of the mini-project. For many participants, examination by written examination; mini-project as prerequisite for participation in the examination. In the written or oral examination, the methods, procedures, pitfalls and legal foundations of data mining are examined. In the mini-project the ability to act independently and on one's own responsibility and the use of suitable software will be tested. |
|---|---|
| Minimum standard | Basic knowledge of the general approach to data mining, the procedures covered and their limitations. |
| Exam Type | EN andere summarische Prüfungsform |
Learning goals
| Goal type | Description |
|---|---|
| Knowledge | Introduction to a suitable software, e.g. Python Introduction to descriptive statistics and possibly also probability calculation Supervised learning: - Classification procedure: Procedure, performance measures, application of a method of instance-based learning, e.g. k-nearest-neighbor and a method of model-based learning, e.g. decision trees - Possibly regression analysis: about machine learning and classical Unsupervised learning: - Cluster analysis: k-means, possibly also DBSCAN Preprocessing of the data: - Handling Damaged / Missing Data - Runaway or noise - problems - Scaling - Visualization of data - Possible dimension reduction - Assessment of data quality - possibly look at different types of data records, make reference to NoSql databases, Outlook on current research, e.g. image recognition, Natural Language Processing, Reinforcement Learning |
| Skills | Be able to name and apply a suitable method and overall approach to tasks Select and evaluate a suitable performance measure Apply Privacy Policy |
Expenditure classroom teaching
| Type | Attendance (h/Wk.) |
|---|---|
| Lecture | 2 |
| Exercises (whole course) | 0 |
| Exercises (shared course) | 2 |
| Tutorial (voluntary) | 0 |
Special literature
| A. Geron: Praxiseinstieg Machine Learning mit Scikit-Learn und TensorFlow: Konzepte, Tools und Techniken für intelligente Systeme, Heidelberg, o‘Reilly Verlag 2017, 978-3960090618, S. Raschka, V. Mirjalili: Machine Learning mit Python und Scikit-Learn und TensorFlow: Das umfassende Praxis-Handbuch für Data Science, Predictive Analytics und Deep Learning, mitp Verlag, 2018, 978-3958457331, J. Frochte, Jörg: Maschinelles Lernen, München, Carl Hanser Verlag GmbH & Co. KG, 2018, eBook ISBN: 978-3-446-45705-8, Print ISBN: 978-3-446-45291-6, A. Müller: Einführung in Machine Learning mit Python: Praxiswissen Data Science, Heidelberg, o‘Reilly Verlag 2017, eBook: 978-3-96010-111-6 |
Special requirements
| none |
| Accompanying material |
Script or set of slides Tasks (expected to be integrated into the script) Mini project task with data set |
|---|---|
| Separate exam | No |
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