623.613 (17S) Applied Machine Learning
Overview
- Lecturer
- Course title german Applied Machine Learning
- Type Lecture - Course (continuous assessment course )
- Hours per Week 2.0
- ECTS credits 4.0
- Registrations 11 (25 max.)
- Organisational unit
- Language of instruction English
- possible language(s) of the assessment German
- Course begins on 22.05.2017
- eLearning Go to Moodle course
Time and place
Course Information
Intended learning outcomes
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.
Teaching methodology including the use of 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.
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
Literature
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.
Examination information
Grading scheme
Grade / Grade grading schemePosition in the curriculum
- 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)
- 623.613 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)
-
Subject: Business Information Systems
(Compulsory elective)
- Master's degree programme Applied Informatics
(SKZ: 911, Version: 13W.1)
-
Subject: Knowledge and Data Engineering
(Compulsory elective)
-
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)
-
Subject: Knowledge and Data Engineering
(Compulsory elective)
- Masterstudium Informatik
(SKZ: 921, Version: 09W.1)
-
Subject: Intelligent Information Systems in Production, Operation and Management (POM)
(Compulsory subject)
-
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)
-
Subject: Intelligent Information Systems in Production, Operation and Management (POM)
(Compulsory subject)
- Master's degree programme Informatics
(SKZ: 921, Version: 03W.1)
-
Subject: Intelligent Information Systems in Production, Operation and Management (POM)
(Compulsory subject)
-
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)
-
Subject: Intelligent Information Systems in Production, Operation and Management (POM)
(Compulsory subject)