Every summer semester
5 Credit points
For information on the duration of the modul, refer to the courses of study in which the module is used.
Non-official translation of the module descriptions. Only the German version is legally binding.
On completion of the module, students will have in-depth knowledge of data mining methods and their theoretical background: statistical methods for extracting correlations and models, complex learning architectures for model extraction, information-theoretical aspects of detecting and describing structures in data.
The module offers an in-depth study of data mining methods and mathematical aspects of data analysis.
Competences that can be acquired, for example, in the modules 39-Inf-NN Introduction to Neural Networks and 39-Inf-10 Database Systems.
Competences such as those acquired in module 39-Inf-DM Introduction to Data Mining are necessary for a successful degree in this module.
—
In some degree programmes, the module (partial) examination can also be "ungraded" at the student's discretion. A corresponding specification must be made before the module is taken; a subsequent change (graded - ungraded) is not possible. If the ungraded option is selected, it is not possible to use this module for a degree programme in which this module is taken into account in the overall grade calculation.
Module structure: 0-1 bPr, 0-1 uPr 1
In some degree programmes of the Faculty of Technology, the module examination can also be "ungraded" at the student's discretion (see explanations of the module elements and the respective subject-specific regulations). If the ungraded option is selected, it is not possible to use this module for a degree programme in which this module is taken into account in the overall grade calculation.
See below for explanations of this examination (graded examination option).
Portfolio of exercises that are set during the course (pass mark 50% of the achievable points, individual explanation of the solutions). The exercises in the portfolio are usually handed out weekly. Final oral examination (15-25 min.) on the contents of the lecture and tutorials (in connection with lecture/seminar).
| Degree programme | Profile | Recommended start 3 | Duration | Mandatory option 4 |
|---|---|---|---|---|
| Data Science / Master of Science [FsB vom 06.04.2018 mit Änderungen vom 01.07.2019, 02.03.2020, 21.03.2023 und 10.12.2024] | Variante 1 | 2. | one semester | Compulsory optional subject |
| Data Science / Master of Science [FsB vom 06.04.2018 mit Änderungen vom 01.07.2019, 02.03.2020, 21.03.2023 und 10.12.2024] | Variante 2 | 2. | one semester | Compulsory optional subject |
| Intelligent Systems / Master of Science [FsB vom 27.07.2018 mit Änderung vom 04.06.2020] | - | 2. | one semester | Compulsory optional subject |
| Intelligent Systems / Master of Science [FsB vom 17.12.2012 mit Änderungen vom 15.04.2013, 01.04.2014, 15.10.2014, 02.03.2015 und Berichtigung vom 17.11.2014] | - | 2. | one semester | Compulsory optional subject |
| Informatics for the Natural Sciences / Master of Science [FsB vom 30.09.2016 mit Berichtigung vom 10.01.2017 und Änderungen vom 15.09.2017, 02.05.2018, 04.06.2020 und 31.03.2023] | - | 2. | one semester | Compulsory optional subject |
| Informatics for the Natural Sciences / Master of Science [FsB vom 17.12.2012 mit Änderungen vom 15.04.2013, 01.04.2014, 15.10.2014, 02.03.2015, 01.12.2015 und Berichtigungen vom 01.04.2014, 17.11.2014 und 12.07.2017] | - | 2. | one semester | Compulsory optional subject |
The system can perform an automatic check for completeness for this module.
This website uses cookies and similar technologies. Some of these are essential to ensure the functionality of the website, while others help us to improve the website and your experience. If you consent, we also use cookies and data to measure your interactions with our website. You can view and withdraw your consent at any time with future effect at our Privacy Policy site. Here you will also find additional information about the cookies and technologies used.