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 Foundations
    sub title: Supervised, Unsupervised, and Advanced Learning
  name:
    name part: Jo, Taeho.
    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 (XX, 391 pages 277 illustrations, 13 illustrations in color.)
  abstract: This book provides conceptual understanding of machine learning algorithms though supervised, unsupervised, and advanced learning techniques. The book consists of four parts: foundation, supervised learning, unsupervised learning, and advanced learning. The first part provides the fundamental materials, background, and simple machine learning algorithms, as the preparation for studying machine learning algorithms. The second and the third parts provide understanding of the supervised learning algorithms and the unsupervised learning algorithms as the core parts. The last part provides advanced machine learning algorithms: ensemble learning, semi-supervised learning, temporal learning, and reinforced learning. Provides comprehensive coverage of both learning algorithms: supervised and unsupervised learning; Outlines the computation paradigm for solving classification, regression, and clustering; Features essential techniques for building the a new generation of machine learning.
  table of contents: Part I. Foundation -- Chapter 1. Introduction -- Chapter 2. Numerical Vectors -- Chapter 3.Data Encoding -- Chapter 4. Simple Machine Learning Algorithms -- Part II. Supervised Learning -- Chapter 5. Instance based Learning -- Chapter 6. Probabilistic Learning -- Chapter 7. Decision Tree -- Chapter 8. Support Vector Machine -- Part III. Unsupervised Learning -- Chapter 9. Simple Clustering Algorithms -- Chapter 10. K Means Algorithm -- Chapter 11. EM Algorithm -- Chapter 12. Advanced Clustering -- Part IV. Advanced Topics -- Chapter 13. Ensemble Learning -- Chapter 14. Semi-Supervised Learning -- Chapter 15. Temporal Learning -- Chapter 16. Reinforcement Learning.
  note: by Taeho Jo.
  subject:
    topic: Electrical engineering
  subject:
    topic: Computational intelligence
  subject:
    topic: Data mining
  subject:
    topic: Information storage and retrieval
  subject:
    topic: Big data
  subject:
    topic: Communications Engineering, Networks
  subject:
    topic: Computational Intelligence
  subject:
    topic: Data Mining and Knowledge Discovery
  subject:
    topic: Information Storage and Retrieval
  subject:
    topic: Big Data/Analytics
  classification: TK1-9971
  classification: 621.382
  related item:
    title info:
      title: Springer Nature eBook
  identifier: 9783030659004
  identifier: http://proxy.library.tamu.edu/login
  record info:
    record creation date: 210212
    record change date: 20230330185508.0
    record identifier: in00004419381