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Machine Learning for Engineers : Introduction to Physics-Informed, Explainable Learning Methods for AI in Engineering Applications - Marcus J. Neuer

Machine Learning for Engineers

Introduction to Physics-Informed, Explainable Learning Methods for AI in Engineering Applications

By: Marcus J. Neuer

eText | 29 November 2024

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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.

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You Can Find This eBook In

Non-FictionComputing & I.T.Computer ScienceArtificial IntelligenceMathematicsProbability & StatisticsDatabasesEngineering & TechnologyTechnology in GeneralEngineering in GeneralMaths for EngineersApplied Mathematics

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