About this book series

Books published in this series focus on the theory and computational foundations, advanced methodologies and practical applications of machine learning, ideally combining mathematically rigorous treatments of a contemporary topics in machine learning with specific illustrations in relevant algorithm designs and demonstrations in real-world applications. The intended readership includes research students and researchers in computer science, computer engineering, electrical engineering, data science, and related areas seeking a convenient medium to track the progresses made in the foundations, methodologies, and applications of machine learning.

Topics considered include all areas of machine learning, including but not limited to:

  • Decision tree
  • Artificial neural networks
  • Kernel learning
  • Bayesian learning
  • Ensemble methods
  • Dimension reduction and metric learning
  • Reinforcement learning
  • Meta learning and learning to learn
  • Imitation learning
  • Computational learning theory
  • Probabilistic graphical models
  • Transfer learning
  • Multi-view and multi-task learning
  • Graph neural networks
  • Generative adversarial networks
  • Federated learning

This series includes monographs, introductory and advanced textbooks, and state-of-the-art collections. Furthermore, it supports Open Access publication mode.

Electronic ISSN
Print ISSN
Series Editor
  • Kay Chen Tan,
  • Dacheng Tao

Book titles in this series