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{ "item_title" : "Machine Learning for Network Management", "item_author" : [" Oscar Caicedo Rendon "], "item_description" : "This book details how machine learning (ML) can be used in network management. Geared towards an upper undergraduate or graduate level class in machine learning or computer networks, the author showcases the intersection between ML/artificial intelligence (AI) and network management paradigms. The book includes practical methodologies and guidelines to realizing AI/ML-driven network management. The book also provides a landscape of AI/ML techniques more relevant to network management while also presenting AI/ML-based network management solutions deployed in production environments so students can explore applications. The book features homework problems, exercises, case studies, and PowerPoint slides.", "item_img_path" : "https://covers4.booksamillion.com/covers/bam/3/03/215/175/3032151759_b.jpg", "price_data" : { "retail_price" : "119.99", "online_price" : "119.99", "our_price" : "119.99", "club_price" : "119.99", "savings_pct" : "0", "savings_amt" : "0.00", "club_savings_pct" : "0", "club_savings_amt" : "0.00", "discount_pct" : "10", "store_price" : "" } }
Machine Learning for Network Management|Oscar Caicedo Rendon

Machine Learning for Network Management

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Overview

This book details how machine learning (ML) can be used in network management. Geared towards an upper undergraduate or graduate level class in machine learning or computer networks, the author showcases the intersection between ML/artificial intelligence (AI) and network management paradigms. The book includes practical methodologies and guidelines to realizing AI/ML-driven network management. The book also provides a landscape of AI/ML techniques more relevant to network management while also presenting AI/ML-based network management solutions deployed in production environments so students can explore applications. The book features homework problems, exercises, case studies, and PowerPoint slides.

This item is Non-Returnable

Details

  • ISBN-13: 9783032151759
  • ISBN-10: 3032151759
  • Publisher: Springer
  • Publish Date: July 2026
  • Page Count: 179

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