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Machine Learning Foundations (eBook)

Supervised, Unsupervised, and Advanced Learning
Artikelnummer: 978-3-030-65900-4
Einband: PDF
Verfügbarkeit: Download, sofort verfügbar (Link per E-Mail)
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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.


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.


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VerlagSpringer Nature Switzerland
EinbandPDF
Erscheinungsjahr2021
Seitenangabe391 S.
AusgabekennzeichenEnglisch
AbbildungenXX, 391 p. 277 illus., 13 illus. in color.
Masse11'079 KB
PlattformPDF
ReiheEngineering; Engineering
AutorJo, Taeho

Alle Bände der Reihe "Engineering; Engineering (R0)"

Über den Autor Taeho Jo

Dr. Taeho Jo The author of this book, Taeho Jo, is the founder of the publishing company, Alpha AI Publication, to which the copyright of this book belongs to, and a professor in KENTECH (Korea Institute of Energy Technology). His specialty is artificial intelligence; he got a Bachelor from Korea University, a Master from POSTECH (Pohang University of Science and Technology), and a PhD from University of Ottawa. He has careers in both industrial organizations, Samsung SDS, ETRI (Electronic and Telecommunication Research Institute), and KISTI (Korea Institute of Science and Technology Information) and academic organizations, Inha University, Hongik University, and KENTECH as a professor. He has published more than 260 research papers and 30 books with almost solo-author, awarded three times in Marquis Who's Who in the World, and granted the noble title, Duke, from United Kingdom in 2018. The author of this book, Taeho Jo, has a very strong vision for the future as a pioneer of artificial intelligence; he recently established the international organization, UAIN (United Artificial Intelligence Nation) for propagating AI techniques globally.

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