Leseprobe
List of Content
List of Content
List of Figures
1 Introduction
1.1 Objective
1.2 Structure of Thesis
1.3 Research Method and Study Design
2 Impact of Machine Learning in Industrie 4.0
2.1 Market Pull is Changing the World of Manufacturing
2.2 Key Challenges for Production Systems in an Evolving Business World
2.3 Industrie
2.4 Ubiquitous Computing and Visualization
2.5 Impact on Human Employment
2.5.1 Computerisation in Non-Routine Manual Tasks
2.5.2 Computerisation in Non-Routine Cognitive Tasks
2.5.3 Implications for Employment
3 Paradigm Shift from Abstract Models to Real World Data
3.1 What Machine Learning is and Why it is a Promising Approach
3.2 Machine Learning Techniques
3.2.1 Regression
3.2.2 Classification and Clustering
3.2.3 Dimensionality reduction
3.2.4 Association rule mining
3.3 Learning Types
3.3.1 Supervised Learning
3.3.2 Unsupervised Learning
3.3.3 Reinforcement Learning
3.4 Algorithm Selection: Implicit vs. Explicit Knowledge Representation
4 Applications of Machine Learning in Production
4.1 Descriptive Analytics
4.2 Diagnostic Analytics
4.3 Predictive Analytics
4.4 Prescriptive Analytics
5 Guidelines for the Usage of Machine Learning in Production
5.1 Domain Maturity: Machine Learning
5.2 Domain Maturity: Production
5.3 Infrastructure: Connection Task
5.4 Data: Capturing Task
5.5 Security: Cyber Security and Accountability Task
5.6 People: Knowledge and Acceptance Task
5.7 Strategy: Cooperate Design Task
6 References
- Arbeit zitieren
- Alexander Volz (Autor:in), 2017, Machine Learning. A Guideline for its Usability in Production Systems, München, GRIN Verlag, https://www.grin.com/document/489418
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