Chapter 7: Predicting Project Results Using Machine Learning Algorithms and Supervised Learning to Predict Results
Chapter in the book
Publisher:Mercury Learning and Information
Paid access
|Nov 2024
Table of contents
Frontmatter
Contents
Preface
Acknowledgments
About the Author
Chapter 1: Why Project Management Needs AI
Chapter 2: Two AI Components for Projects
Chapter 3: The Business Case for AI
Chapter 4: Automating Project Management Tasks
Chapter 5: Providing Good Project Data
Chapter 6: Acquiring and Using Data
Chapter 7: Predicting Project Results Using Machine Learning Algorithms and Supervised Learning to Predict Results
Chapter 8: Improving Project Productivity with NLP
Chapter 9: Generative AI and Large Language Models
Chapter 10: Genetic Algorithms for Project Navigation
Chapter 11: Project Initiation, Planning, Delivery, and Close
Chapter 12: Project Control and Project Termination
Chapter 13: AI for Agile Process Effectiveness
Chapter 14: Applying AI to Resolve Project Failure
Chapter 15: The Build or Buy Decision
Chapter 16: Evaluating and Acquiring AI Software
Chapter 17: Implementing AI Solutions
Chapter 18: Changes to Roles of the Project Manager, PMO, and Project Team
Chapter 19: Ethical Implications of AI in Project Management
Chapter 20: The Rapid Advance of AI Technology
Chapter 21: Conclusion
Appendix: Terms and Definitions
Index
28 chapters available
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PDF ISBN: 978-1-5015-1930-7 | E-Pub ISBN: 978-1-5015-1940-6 | Paperback ISBN: 978-1-5015-2270-3 | DOI: 10.1515/9781501519307
Publisher: Mercury Learning and Information
Copyright owner: © 2024 Walter de Gruyter GmbH, Berlin/Boston
Publication date: 2024
Language: English
Pages: 220