623.613 (17S) 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 11 (25 max.)
- Organisationseinheit
- Unterrichtssprache Englisch
- mögliche Sprache/n der Leistungserbringung Deutsch
- LV-Beginn 22.05.2017
- eLearning zum Moodle-Kurs
Zeit und Ort
LV-Beschreibung
Intendierte Lernergebnisse
The course provides a practical introduction into machine learning methods applied in computer science.
Note: The course appointments are blocked and are planned for May.
Lehrmethodik inkl. Einsatz von eLearning-Tools
The course is build on top of the "Uncertain Knowledge" (621.065) and assumes the knowledge of the presented material.
In addition, it is recommended to visit the course "Selected Topics in Artificial Intelligence" (623.131) for an in-depth review of data analysis methods as well as learning of bayesian networks, rule-sets and decision trees.
Knowledge of R and/or Python is a plus.
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
Literatur
Please also consider visiting the course "Selected Topics in Knowlegde and Data Engineering: Data Mining" (623.253) given by Prof. Morzy for an in-depth review of data acquisition and manipulation methods.
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
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)
- Masterstudium Informatik
(SKZ: 921, Version: 09W.1)
-
Fach: Intelligent Information Systems in Production, Operation and Management (POM)
(Pflichtfach)
-
Weitere Lehrveranstaltungen aus dem Spezialisierungsfach (
4.0h XX / 8.0 ECTS)
- 623.613 Applied Machine Learning (2.0h VC / 4.0 ECTS)
-
Weitere Lehrveranstaltungen aus dem Spezialisierungsfach (
4.0h XX / 8.0 ECTS)
-
Fach: Intelligent Information Systems in Production, Operation and Management (POM)
(Pflichtfach)
- Masterstudium Informatik
(SKZ: 921, Version: 03W.1)
-
Fach: Intelligent Information Systems in Production, Operation and Management (POM)
(Pflichtfach)
-
Weitere Lehrveranstaltungen aus dem Spezialisierungsfach (
4.0h XX / 8.0 ECTS)
- 623.613 Applied Machine Learning (2.0h VC / 4.0 ECTS)
-
Weitere Lehrveranstaltungen aus dem Spezialisierungsfach (
4.0h XX / 8.0 ECTS)
-
Fach: Intelligent Information Systems in Production, Operation and Management (POM)
(Pflichtfach)