rdf-syntax-grammar
mods:
  schema location: http://www.loc.gov/mods/v3 http://www.loc.gov/standards/mods/v3/mods-3-2.xsd
  title info:
    title: Machine Learning for Engineers
    sub title: Using data to solve problems for physical systems
  name:
    name part: McClarren, Ryan G.
    role:
      role term: creator
    role:
      role term: author.
    role:
      role term: aut
    role:
      role term: http://id.loc.gov/vocabulary/relators/aut
  name:
    name part: SpringerLink (Online service)
  type of resource: text
  genre: Electronic books.
  origin info:
    place:
      place term: sz
    date issued: 2021
    edition: 1st ed. 2021.
    issuance: monographic
  language:
    language term: eng
  physical description:
    reformatting quality: access
    form: remote
    extent: 1 online resource (XIII, 247 pages 106 illustrations, 90 illustrations in color.)
  abstract: All engineers and applied scientists will need to harness the power of machine learning to solve the highly complex and data intensive problems now emerging. This text teaches state-of-the-art machine learning technologies to students and practicing engineers from the traditionally "analog" disciplines-mechanical, aerospace, chemical, nuclear, and civil. Dr. McClarren examines these technologies from an engineering perspective and illustrates their specific value to engineers by presenting concrete examples based on physical systems. The book proceeds from basic learning models to deep neural networks, gradually increasing readers' ability to apply modern machine learning techniques to their current work and to prepare them for future, as yet unknown, problems. Rather than taking a black box approach, the author teaches a broad range of techniques while conveying the kinds of problems best addressed by each. Examples and case studies in controls, dynamics, heat transfer, and other engineering applications are implemented in Python and the libraries scikit-learn and tensorflow, demonstrating how readers can apply the most up-to-date methods to their own problems. The book equally benefits undergraduate engineering students who wish to acquire the skills required by future employers, and practicing engineers who wish to expand and update their problem-solving toolkit.
  table of contents: Part I Fundamentals -- 1. Introduction -- 2. The landscape of machine learning -- 3. Linear models -- 4. Tree-based models -- 5. Clustering data -- Part II Deep Neural Networks -- 6. Feed-forward Neural networks -- 7.convolutional neural networks -- 8. Recurrent neural networks for time series data -- Part III Advanced topics in machine learning -- 9. Unsupervised learning with neural networks -- 10. Reinforcement learning -- 11. Transfer learning -- Part IV Appendixes -- Appendix A. Sci-Kit learn -- Appendix B. Tensorflow.
  note: by Ryan G. McClarren.
  subject:
    topic: Computational intelligence
  subject:
    topic: Computer mathematics
  subject:
    topic: Artificial intelligence
  subject:
    topic: Mechanical engineering
  subject:
    topic: Civil engineering
  subject:
    topic: Nuclear energy
  subject:
    topic: Computational Intelligence
  subject:
    topic: Computational Science and Engineering
  subject:
    topic: Artificial Intelligence
  subject:
    topic: Mechanical Engineering
  subject:
    topic: Civil Engineering
  subject:
    topic: Nuclear Energy
  classification: Q342
  classification: 006.3
  related item:
    title info:
      title: Springer Nature eBook
  identifier: 9783030703882
  identifier: http://proxy.library.tamu.edu/login
  record info:
    record creation date: 210921
    record change date: 20230330184948.1
    record identifier: in00004393526