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Machine Learning Algorithms for Engineering Applications: Future Trends and Research Directions

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Title: Machine Learning Algorithms for Engineering Applications: Future Trends and Research Directions
Description: Machine learning is a vital part of numerous academic and financial applications, in areas ranging from health care and treatment to finding relevant information in social networks. Large organizations thoughtfully apply machine learning algorithms with extensive research teams. The purpose of this book is to provide an intellectual introduction to statistical or machine learning (ML) techniques for those that would not normally be exposed to such approaches during their typical required statistical exercise. Statistical analysis is an integral part of machine learning and can be described as a form of it, often even utilizing well-known and familiar techniques, that has a different focus than traditional analytical practice in applied disciplines. The key notion is that flexible, automatic approaches are used to detect patterns within the data, with a primary focus on making predictions on future data.
Authors: Prasenjit Chatterjee
Resource Type: eBook.
Subjects: Engineering--Data processing, Artificial intelligence, Machine learning
Categories: COMPUTERS / Data Science / Machine Learning
Database: eBook Collection (EBSCOhost)
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  Data: Machine learning is a vital part of numerous academic and financial applications, in areas ranging from health care and treatment to finding relevant information in social networks. Large organizations thoughtfully apply machine learning algorithms with extensive research teams. The purpose of this book is to provide an intellectual introduction to statistical or machine learning (ML) techniques for those that would not normally be exposed to such approaches during their typical required statistical exercise. Statistical analysis is an integral part of machine learning and can be described as a form of it, often even utilizing well-known and familiar techniques, that has a different focus than traditional analytical practice in applied disciplines. The key notion is that flexible, automatic approaches are used to detect patterns within the data, with a primary focus on making predictions on future data.
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        Type: prePub
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      – Code: eng
        Text: English
    Subjects:
      – SubjectFull: Engineering--Data processing
        Type: general
      – SubjectFull: Artificial intelligence
        Type: general
      – SubjectFull: Machine learning
        Type: general
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      – TitleFull: Machine Learning Algorithms for Engineering Applications: Future Trends and Research Directions
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            NameFull: Prasenjit Chatterjee
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          Dates:
            – D: 01
              M: 01
              Type: published
              Y: 2022
            – D: 23
              M: 12
              Type: profile
              Y: 2022
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              Value: 9798886970869
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