About this book series
Series Editor: Oge Marques
The Springer Series in Applied Machine Learning focuses on monographs, textbooks, edited volumes, and reference books that provide suitable content and educate the reader on how the theoretical approaches, algorithms, and techniques of machine learning can be applied to address real-world problems in a principled way.
The goal is a series of state-of-the-art books for the libraries of both machine learning scientists interested in applying the latest techniques to novel problems as well as practitioners, technologists, and researchers in various fields interested in leveraging the latest advances in machine learning for developing solutions that work for practical problems. The scope spans the breadth of machine learning and AI as it pertains to all application areas ─ both through books that address techniques specific to one application domain ─ and books that show the applicability of different types of machine learning methods to a wide array of domains.
- Electronic ISSN
- 2520-1301
- Print ISSN
- 2520-1298
- Series Editor
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- Oge Marques
