623.613 (19S) Applied Machine Learning
Überblick
- Lehrende/r
- LV-Titel englisch Applied Machine Learning
- LV-Art Vorlesung-Kurs (prüfungsimmanente LV )
- Semesterstunde/n 2.0
- ECTS-Anrechnungspunkte 4.0
- Anmeldungen 18 (25 max.)
- Organisationseinheit
- Unterrichtssprache Englisch
- mögliche Sprache/n der Leistungserbringung Deutsch
- LV-Beginn 06.03.2019
- eLearning zum Moodle-Kurs
- Studienberechtigungsprüfung Ja
- 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.
Please consider visiting:
- "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.
Lehrmethodik inkl. Einsatz von eLearning-Tools
Lectures with a student's project applying machine learning.
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
The basics of the probability theory given in, e.g., "Uncertain Knowledge" (621.065), Stochastik 1/2, etc.
Knowledge of R and/or Python is a plus.
Please consider visiting:
- "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.
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
- 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)
- 623.613 Applied Machine Learning (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 Angewandte Informatik
(SKZ: 911, Version: 13W.1)
-
Fach: Knowledge and Data Engineering
(Wahlfach)
-
Knowledge Representation and Reasoning for Games (
2.0h VK / 4.0 ECTS)
- 623.613 Applied Machine Learning (2.0h VC / 4.0 ECTS)
-
Knowledge Representation and Reasoning for Games (
2.0h VK / 4.0 ECTS)
-
Fach: Knowledge and Data Engineering
(Wahlfach)
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