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Machine Learning Foundations de Taeho Jo

Machine Learning Foundations

Autor Taeho Jo
en Limba Engleză Hardback – 13 feb 2021
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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Specificații

ISBN-13: 9783030658991
ISBN-10: 3030658996
Pagini: 412
Ilustrații: XX, 391 p. 277 illus., 13 illus. in color.
Dimensiuni: 160 x 241 x 28 mm
Greutate: 0.73 kg
Ediția:1st ed. 2021
Editura: Springer
Locul publicării:Cham, Switzerland

Cuprins

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.

Notă biografică

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.

Textul de pe ultima copertă

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.