623.616 (19W) Topics in Knowledge and Data Engineering for Business Information Systems
Overview
- Lecturer
- Course title german Topics in Knowledge and Data Engineering for Business Information Systems
- Type Lecture - Course (continuous assessment course )
- Hours per Week 2.0
- ECTS credits 4.0
- Registrations 20 (30 max.)
- Organisational unit
- Language of instruction English
- possible language(s) of the assessment German
- Course begins on 02.10.2019
- eLearning Go to Moodle course
- Seniorstudium Liberale Yes
Time and place
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Course Information
Intended learning outcomes
The course provides a practical introduction into machine learning methods applied in computer science using Python programming language.
Teaching methodology including the use of 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.
Course content
- Introduction to machine learning
- Supervised learning: classification and regression
- Unsupervised learning: transformation of data and clustering
- Validation of models
- Overview of reinforcement learning
Prior knowledge expected
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
Literature
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.
Examination information
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.
Grading scheme
Grade / Grade grading schemePosition in the curriculum
- Bachelor's degree programme Applied Informatics
(SKZ: 511, Version: 19W.2)
-
Subject: Artificial Intelligence
(Compulsory elective)
-
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)
-
Subject: Artificial Intelligence
(Compulsory elective)
- Master's degree programme Applied Informatics
(SKZ: 911, Version: 13W.1)
-
Subject: Business Information Systems
(Compulsory elective)
-
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)
-
Subject: Business Information Systems
(Compulsory elective)
- Master's degree programme Informatics
(SKZ: 911, Version: 19W.2)
-
Subject: Business Information Systems
(Compulsory elective)
-
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)
-
Subject: Business Information Systems
(Compulsory elective)
- Master's degree programme Information Management
(SKZ: 922, Version: 19W.1)
-
Subject: Information and IT Management
(Compulsory subject)
-
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)
-
Subject: Information and IT Management
(Compulsory subject)
- Master's degree programme Information Management
(SKZ: 922, Version: 13W.2)
-
Subject: Informations- und IT- Management
(Compulsory subject)
-
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)
-
Subject: Informations- und IT- Management
(Compulsory subject)
Equivalent courses for counting the examination attempts
This course is not assigned to a sequence of equivalent courses