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Machine Learning Foundations - Taeho Jo

Machine Learning Foundations

Supervised, Unsupervised, and Advanced Learning

(Autor)

Buch | Softcover
XX, 391 Seiten
2022 | 1st ed. 2021
Springer International Publishing (Verlag)
978-3-030-65902-8 (ISBN)
CHF 224,65 inkl. MwSt
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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.

lt;p> Taeho Jo is the president and the founder of the company, Alpha Lab AI which makes business concerned with Artificial Intelligence. He received his Bachelor, Master, and PhD degrees from Korea University in 1994, from Pohang University in 1997, and from University of Ottawa, 2006, respectively. He has published more than 180 research papers, primarily in text mining, machine learning, neural networks, and information retrieval. He previously published the book "Text Mining: Concept, Implementation, and Big Data Challenge" (Springer 2018).

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.

Erscheinungsdatum
Zusatzinfo XX, 391 p. 277 illus., 13 illus. in color.
Verlagsort Cham
Sprache englisch
Maße 155 x 235 mm
Gewicht 629 g
Themenwelt Technik Elektrotechnik / Energietechnik
Technik Nachrichtentechnik
Schlagworte EM algorithm • Fuzzy Clustering • hierarchical clustering • K Means Algorithm • K nearest Neighbor • machine learning • naïve bayes • Neural networks • supervised learning • Support Vector Machine • Unsupervised Learning
ISBN-10 3-030-65902-X / 303065902X
ISBN-13 978-3-030-65902-8 / 9783030659028
Zustand Neuware
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