Applied Artificial Intelligence
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Applied Artificial Intelligence

An Engineering Approach

About the Book

Why yet another book on Artificial Intelligence? 

It is true that hundreds of publications on Artificial Intelligence (AI) have been published within the last decades - scientific papers and text books. Most of them focus on the theory behind AI solutions: logic, reasoning, statistical foundations, etc. However, little can be found on engineering AI applications.

Modern, complex IT applications are not built from scratch but by integrating off-the-shelf components: libraries, frameworks, and services. The same applies, of course, for AI applications. Over the last decades, numerous  off-the-shelf components for AI base functionality such as machine learning, logic reasoning, natural language procesing and computer vision have been implemented - commercial and open source. Integrating such components into user friendly, high-performance, and maintainable AI applications requires specific engineering skills. "Applied Artificial Intelligence - An Engingeering Approach" focuses on those skills.

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    • Software
    • Software Engineering
    • Data Science
    • Computer Science
    • Software Architecture
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About the Author

Bernhard Humm
Bernhard G. Humm

Bernhard G. Humm is a professor for software engineering and project management at the Computer Science Department of Hochschule Darmstadt - University of Applied Sciences, Germany. He received a Ph.D. from the University of Wollongong, Australia and the degree Dipl.-Inform. from Kaiserslautern University, Germany.  His research focus is on semantic applications, software architecture, and programming languages. He is a member of the board of directors of the institute of applied informatics, Darmstadt (aIDa) and Ph.D. coordinator.  He is running several national and international research projects in co-operation with industry and research organizations and is publishing his results regularly. 

Before re-entering university as a professor, he worked in the IT industry for 11 years, as a software architect, chief consultant, IT manager and head of the research department of a large German software company. His clients were in the industry sectors finance, tourism, trade, and aviation.

Contact:   Prof. Bernhard G. Humm, Dipl.-Inform., Ph.D. Computer Science (AUS)   Hochschule Darmstadt - University of Applied Sciences, Computer Science Department   Haardring 100, 64295 Darmstadt, Germany,

Table of Contents

  • Preface
  • Copyright Notice
  • 1. Introduction
    • 1.1 Overview of this Book
    • 1.2 What is AI?
    • 1.3 A Brief History of AI
    • 1.4 Impacts of AI on Society
    • 1.5 Prominent AI Projects
    • 1.6 Further Reading
    • 1.7 Quick Check
  • 2. Machine Learning
    • 2.1 ML Applications
    • 2.2 ML Areas and Tasks
    • 2.3 ML Approaches
    • 2.4 Example: Classifying Customers using Decision Tree Learning
    • 2.5 ML Methodology
    • 2.6 Automated Machine Learning
    • 2.7 Services Maps and Product Maps
    • 2.8 Engineering ML Applications
    • 2.9 Quick Check
  • 3. Knowledge Representation
    • 3.1 Knowledge Graphs
    • 3.2 Knowledge Representation Approaches
    • 3.3 Semantic Web Standards
    • 3.4 Querying knowledge graphs
    • 3.5 Rule-based Reasoning
    • 3.6 Knowledge Representation Services and Product Maps
    • 3.7 Tips and Tricks
    • 3.8 Quick Check
  • 4. AI Application Architecture
    • 4.1 AI Reference Architecture
    • 4.2 Application Example: Virtual Museum Guide
    • 4.3 Data Integration / Semantic Enrichment
    • 4.4 Application Logic / Agents
    • 4.5 Presentation
    • 4.6 Programming Languages
    • 4.7 Quick Check
  • 5. Information Retrieval
    • 5.1 Information Retrieval Services Map
    • 5.2 Information Retrieval Product Map
    • 5.3 Tips and Tricks
    • 5.4 Application Example: Semantic Autosuggest Feature
    • 5.5 Quick Check
  • 6. Natural Language Processing
    • 6.1 The Big Picture
    • 6.2 Simple Approach: Bag-of-words Model
    • 6.3 Deeper Semantic Analysis: From Letters to Sentences
    • 6.4 Services and Product Maps
    • 6.5 Examples
    • 6.6 Quick Check
  • 7. Computer Vision
    • 7.1 Computer Vision Applications
    • 7.2 Computer Vision Tasks and Approaches
    • 7.3 Services and Product Maps
    • 7.4 Examples
    • 7.5 Quick Check
  • 8. Complex Event Processing
    • 8.1 Foundations
    • 8.2 Application Example: Fault Detection in the Smart Factory
    • 8.3 Services Map and Product Map
    • 8.4 Quick Check
  • 9. Conclusions
  • Literature
  • Appendix: Product Tables
    • Machine Learning
    • Knowledge Representation
    • AI Application Architecture
    • Agent Frameworks
    • Information Retrieval
    • Natural Language Processing
    • Computer Vision
    • Complex Event Processing
  • About the Author
  • Notes

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