Statistical Machine Learning for Engineering with Applications
This book offers a leisurely introduction to the concepts and methods of machine learning. Readers will learn about classification trees, Bayesian learning, neural networks and deep learning, the design of experiments, and related methods. For ease of reading, technical details are avoided as far as...
Gorde:
| Erakunde egilea: | |
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| Beste egile batzuk: | , |
| Formatua: | Baliabide elektronikoa eBook |
| Hizkuntza: | ingelesa |
| Argitaratua: |
Cham :
Springer Nature Switzerland : Imprint: Springer,
2024.
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| Edizioa: | 1st ed. 2024. |
| Saila: | Lecture Notes in Statistics,
227 |
| Gaiak: | |
| Sarrera elektronikoa: | https://doi.org/10.1007/978-3-031-66253-9 |
| Etiketak: |
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Aurkibidea:
- - An Introduction of Statistical Learning for Engineers
- Machine Learning for Inline Surface Inspection Systems - Challenges, Approaches, and Application Example
- Gaussian Process Regression for the Prediction of Cable Bundle Characteristics
- Machine Learning for Predictive Maintenance in Production Environments
- Detecting Healthcare Fraud Using Hybrid Machine Learning for Document Digitization
- Cracks in concrete
- Machine learning methods for prediction of breakthrough curves in reactive porous media
- Segmentation and Aggregation in Text Classification
- Hardware-aware Neural Architecture Search
- Optimal Experimental Design Supported by Machine Learning Regression Models
- Data Analytics, Artificial Intelligence and Machine Learning in Mobility and Vehicle Engineering.