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Machine learning foundationssupervis...

Jo, Taeho.

Machine learning foundationssupervised, unsupervised, and advanced learning /

紀錄類型:	書目-電子資源 : Monograph/item
正題名/作者:	Machine learning foundationsby Taeho Jo.
其他題名:	supervised, unsupervised, and advanced learning /
作者:	Jo, Taeho.
出版者:	Cham :Springer International Publishing :2021.
面頁冊數:	xx, 391 p. :ill., digital ;24 cm.
Contained By:	Springer Nature eBook
標題:	Machine learning.
電子資源:	https://doi.org/10.1007/978-3-030-65900-4
ISBN:	9783030659004$q(electronic bk.)

Machine learning foundationssupervised, unsupervised, and advanced learning /
Jo, Taeho.

Machine learning foundationssupervised, unsupervised, and advanced learning /[electronic resource] :by Taeho Jo. - Cham :Springer International Publishing :2021. - xx, 391 p. :ill., digital ;24 cm.

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.

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.

ISBN: 9783030659004$q(electronic bk.)

Standard No.: 10.1007/978-3-030-65900-4doiSubjects--Topical Terms:

188639
Machine learning.

LC Class. No.: Q325.5

Dewey Class. No.: 006.31

Machine learning foundationssupervised, unsupervised, and advanced learning /
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505 0 $a 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.
520 $a 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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