Oscar Mauricio Caicedo Rendon, one of the members of the international research network of INCT ICoNIoT, will release his book “Machine Learning for Network Management” on April 26, 2026. The book is being published by Springer Nature and can be purchased as a complete volume or by individual chapters.
“Machine Learning for Network Management” was written as a guide text for undergraduate students in last year, master’s and doctoral students in computer science and related areas who are interested in studying the application of machine learning in networks, introducing them to the world of algorithms that are useful for solving network management problems.
Book structure
The book is divided into three parts. The first is conceptual, covering ML and network management, and presenting a chronology of ML in network management from the 1950s up to 2024/2025, when it reaches generative AI.
The second part deals specifically with network configuration, covering elements such as performance, security, configuration failures, and the definition of algorithms for each of these areas. Algorithms and code are presented, along with their computational complexity and use cases.
The third and final part presents, in greater depth, an example application of generative AI for network management. It introduces a problem that can be addressed using generative AI, aiming to provide a current, up-to-date case.
In addition to code examples, each chapter presents practical exercises and their solutions, as well as slides that professors can use and customize as they wish.
The book connects with several research efforts developed within ICoNIoT.
About the researcher
Oscar Mauricio Caicedo is a professor at the Faculty of Electronic Engineering and Telecommunications at Universidad del Cauca, Colombia, where he is a member of the Telematics Engineering Group. He earned his PhD at UFRGS under the supervision of researcher Lisandro Granville. When he returned to Colombia in 2015, he began working in the field of network management using machine learning algorithms with Professor Nelson Fonseca—well before the boom in generative AI that has been observed.
In 2018, Prof. Caicedo was already publishing the first results of his research in ML for network management, and he continues to do so in 2026. His book draws on this entire experience of more than ten years of research, using reinforcement learning, deep reinforcement learning, federated learning, explainable artificial intelligence, and large/small language models, among others for solving problems in faults, configuration, performance, and security in communication networks.
To learn more, visit the book’s page on the Springer Nature website