Machine Learning Foundations: Supervised, Unsupervised, and Advanced Learning - Brossura

Jo, Taeho

Brossura

ISBN 10: 303065902X ISBN 13: 9783030659028

Casa editrice: Springer, 2022

Riassunto

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.

Su questo libro

Informazioni sull?autore

<p><b>Taeho Jo </b>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).</p>

Dalla quarta di copertina

<p>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.</p><p></p><ul><li>Provides comprehensive coverage of both learning algorithms: supervised and unsupervised learning;</li><li>Outlines the computation paradigm for solving classification, regression, and clustering;</li><li>Features essential techniques for building the a new generation of machine learning.</li></ul><p></p>

Dettagli dell'edizione

• Editore: Springer
• Data di pubblicazione: 2022
• Lingua: Inglese
• ISBN 10: 303065902X
• ISBN 13: 9783030659028
• Rilegatura: Copertina flessibile
• Numero edizione: 1
• Numero di pagine: 412
• Contatto del produttore: non disponibile
• Persona responsabile: CMN Printpool Inh. Mathis Neumann; https://www.also.com/ec/cms5/en_6000/6000/index.jsp; ; Friedrich-Karl-Str. 9; Hamburg; Germania