Machine Learning for Engineers: Introduction to Physics-Informed, Explainable Learning Methods for AI in Engineering Applications
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Fulfillment optionsMachine Learning for Engineers
Machine learning and artificial intelligence are ubiquitous terms for improving technical processes. However, practical implementation in real-world problems is often difficult and complex.
This textbook explains learning methods based on analytical concepts in conjunction with complete programming examples in Python, always referring to real technical application scenarios. It demonstrates the use of physics-informed learning strategies, the incorporation of uncertainty into modeling, and the development of explainable, trustworthy artificial intelligence with the help of specialized databases.
Therefore, this textbook is aimed at students of engineering, natural science, medicine, and business administration as well as practitioners from industry (especially data scientists), developers of expert databases, and software developers.
Details
- Publisher
- Springer Berlin Heidelberg
- Author
- Marcus J. Neuer
- Format
- Paperback
- Published
- Nov 30, 2024
- ISBN-13
- 9783662699942
- Pages
- 241
- Printing
- Printed on demand
Machine Learning for Engineers for groups: pricing
Volume pricing applies automatically in your cart. No codes, no account needed.
| Copies | Price per copy | You save |
|---|---|---|
| 25 – 49 | $48.39 | 12% off list |
| 50 – 99 | $47.84 | 13% off list |
| 100+ | $47.29 | 14% off list |
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Machine Learning for Engineers: Introduction to Physics-Informed, Explainable Learning Methods for AI in Engineering Applications
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