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 and Artificial Intelligence
  name:
    name part: Joshi, Ameet V.
    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: 2023
    edition: 2nd ed. 2023.
    issuance: monographic
  language:
    language term: eng
  physical description:
    reformatting quality: access
    form: remote
    extent: 1 online resource (XXI, 271 pages 129 illustrations, 125 illustrations in color)
  abstract: The new edition of this popular professional book on artificial intelligence (ML) and machine learning (ML) has been revised for classroom or training use. The new edition provides comprehensive coverage of combined AI and ML theory and applications. Rather than looking at the field from only a theoretical or only a practical perspective, this book unifies both perspectives to give holistic understanding. The first part introduces the concepts of AI and ML and their origin and current state. The second and third parts delve into conceptual and theoretic aspects of static and dynamic ML techniques. The fourth part describes the practical applications where presented techniques can be applied. The fifth part introduces the user to some of the implementation strategies for solving real life ML problems. Each chapter is accompanied with a set of exercises that will help the reader / student to apply the learnings from the chapter to a real-life problem. Completion of these exercises will help the reader / student to solidify the concepts learned. The book is appropriate for students in graduate and upper undergraduate courses in addition to researchers and professionals. It makes minimal use of mathematics to make the topics more intuitive and accessible. The book covers a large gamut of topics in the area of AI and ML and a professor can tailor a course on AI / ML based on the book by selecting and re-organizing the sequence of chapters to suit the needs.
  table of contents: Introduction -- Introduction to AI and ML -- Essential Concepts in Artificial Intelligence and Machine Learning -- Data Understanding, Representation, and Visualization -- Linear Methods -- Perceptron and Neural Networks -- Decision Trees -- Support Vector Machines -- Probabilistic Models -- Dynamic Programming and Reinforcement Learning -- Evolutionary Algorithms -- Time Series Models -- Deep Learning -- Emerging Trends in Machine Learning -- Unsupervised Learning -- Featurization -- Designing and Tuning -- Model Pipelines -- Performance Measurement -- Classification -- Regression -- Ranking -- Recommendations Systems -- Azure Machine Learning -- Open Source Machine Learning Libraries -- Amazon's Machine Learning Toolkit: Sagemaker -- Conclusion.
  note: by Ameet V Joshi.
  subject:
    topic: Telecommunication
  subject:
    topic: Machine learning
  subject:
    topic: Artificial intelligence
  subject:
    topic: Computational intelligence
  subject:
    topic: Communications Engineering, Networks
  subject:
    topic: Machine Learning
  subject:
    topic: Artificial Intelligence
  subject:
    topic: Computational Intelligence
  classification: TK5101-5105.9
  classification: 621.382
  related item:
    title info:
      title: Springer Nature eBook
  identifier: 9978-3-031-12282-8
  identifier: 9783031122828
  identifier: http://proxy.library.tamu.edu/login
  record info:
    record creation date: 221216
    record change date: 20240805185630.3
    record identifier: in00004653353