623.616 (19W) Topics in Knowledge and Data Engineering for Business Information Systems
Überblick
- Lehrende/r
- LV-Titel englisch Topics in Knowledge and Data Engineering for Business Information Systems
- LV-Art Vorlesung-Kurs (prüfungsimmanente LV )
- Semesterstunde/n 2.0
- ECTS-Anrechnungspunkte 4.0
- Anmeldungen 20 (30 max.)
- Organisationseinheit
- Unterrichtssprache Englisch
- mögliche Sprache/n der Leistungserbringung Deutsch
- LV-Beginn 02.10.2019
- eLearning zum Moodle-Kurs
- Seniorstudium Liberale Ja
Zeit und Ort
Liste der Termine wird geladen...
LV-Beschreibung
Intendierte Lernergebnisse
The course provides a practical introduction into machine learning methods applied in computer science using Python programming language.
Lehrmethodik inkl. Einsatz von eLearning-Tools
The course combines a set of lectures with home work and a project in which students demonstrate their knowledge of machine learning techniques by applying them to solve some practical problem.
Inhalt/e
- Introduction to machine learning
- Supervised learning: classification and regression
- Unsupervised learning: transformation of data and clustering
- Validation of models
- Overview of reinforcement learning
Erwartete Vorkenntnisse
Basics of the probability theory as well as knowledge of R and/or Python is a plus.
After/during the course, please consider visiting also courses providing an in-depth discussion of machine learning techniques:
- "Selected Topics in Artificial Intelligence" (623.131) for an in-depth review of reinforcement learning methods,
- "Selected Topics in Knowledge and Data Engineering: Data Mining" (623.253) for an overview of data acquisition and manipulation methods.
- "Current Topics in Distributed Multimedia Systems: Video Analysis & Retrieval" (623.915) which among other interesting topics considers various architectures and applications of Deep Neural Networks to image/video processing and recognition
Literatur
Beginners:
- James, G., Witten, D., & Hastie, T. (2014). An Introduction to Statistical Learning: With Applications in R. Springer
- Raschka, S. (2015). Python machine learning. Packt Publishing Ltd.
- Wickham, H. and Grolemund G. (2017) R for Data Science. O'Reilly Media Inc.
Classics:
- Mitchell, T. (1997) Machine Learning. McGraw Hill.
- Bishop, C. M. (2006). Pattern Recognition and Machine Learning. Springer.
- Friedman, J., Hastie, T., & Tibshirani, R. (2009). The elements of statistical learning. 2nd edition, Springer.
Prüfungsinformationen
Im Fall von online durchgeführten Prüfungen sind die Standards zu beachten, die die technischen Geräte der Studierenden erfüllen müssen, um an diesen Prüfungen teilnehmen zu können.
Beurteilungsschema
Note BenotungsschemaPosition im Curriculum
- Bachelorstudium Angewandte Informatik
(SKZ: 511, Version: 19W.2)
-
Fach: Artificial Intelligence
(Wahlfach)
-
8.1 Artificial Intelligence (
0.0h XX / 12.0 ECTS)
- 623.616 Topics in Knowledge and Data Engineering for Business Information Systems (2.0h VC / 4.0 ECTS) Absolvierung im 4., 5., 6. Semester empfohlen
-
8.1 Artificial Intelligence (
0.0h XX / 12.0 ECTS)
-
Fach: Artificial Intelligence
(Wahlfach)
- Masterstudium Angewandte Informatik
(SKZ: 911, Version: 13W.1)
-
Fach: Business Information Systems
(Wahlfach)
-
Topics in Knowledge and Data Engineering for Business Information Systems (
2.0h VK / 4.0 ECTS)
- 623.616 Topics in Knowledge and Data Engineering for Business Information Systems (2.0h VC / 4.0 ECTS)
-
Topics in Knowledge and Data Engineering for Business Information Systems (
2.0h VK / 4.0 ECTS)
-
Fach: Business Information Systems
(Wahlfach)
- Masterstudium Informatics
(SKZ: 911, Version: 19W.2)
-
Fach: Business Information Systems
(Wahlfach)
-
Weitere LVen aus dem gewählten Spezialisierungsfach (
0.0h XX / 12.0 ECTS)
- 623.616 Topics in Knowledge and Data Engineering for Business Information Systems (2.0h VC / 4.0 ECTS) Absolvierung im 1., 2. Semester empfohlen
-
Weitere LVen aus dem gewählten Spezialisierungsfach (
0.0h XX / 12.0 ECTS)
-
Fach: Business Information Systems
(Wahlfach)
- Masterstudium Information Management
(SKZ: 922, Version: 19W.1)
-
Fach: Information and IT Management
(Pflichtfach)
-
3.3 Current Topics in Information and IT Management (
0.0h VC, KS, SE / 4.0 ECTS)
- 623.616 Topics in Knowledge and Data Engineering for Business Information Systems (2.0h VC / 4.0 ECTS) Absolvierung im 1., 2., 3. Semester empfohlen
-
3.3 Current Topics in Information and IT Management (
0.0h VC, KS, SE / 4.0 ECTS)
-
Fach: Information and IT Management
(Pflichtfach)
- Masterstudium Informationsmanagement
(SKZ: 922, Version: 13W.2)
-
Fach: Informations- und IT- Management
(Pflichtfach)
-
3.8 Current Topics in Information Management (
2.0h SE/VC/KS / 4.0 ECTS)
- 623.616 Topics in Knowledge and Data Engineering for Business Information Systems (2.0h VC / 4.0 ECTS)
-
3.8 Current Topics in Information Management (
2.0h SE/VC/KS / 4.0 ECTS)
-
Fach: Informations- und IT- Management
(Pflichtfach)
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