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.

Electronic ISSN
2730-9916
Print ISSN
2730-9908
Series Editor
  • Kay Chen Tan,
  • Dacheng Tao

Book titles in this series

  1. Derivative-Free Optimization

    Theoretical Foundations, Algorithms, and Applications

    Authors:
    • Yang Yu
    • Hong Qian
    • Yi-Qi Hu
    • Copyright: 2025

    Available Renditions

    • Hard cover
    • eBook
  2. Embodied Multi-Agent Systems

    Perception, Action, and Learning

    Authors:
    • Huaping Liu
    • Xinzhu Liu
    • Kangyao Huang
    • Di Guo
    • Copyright: 2025

    Available Renditions

    • Hard cover
    • eBook
  3. Evolutionary Multi-Task Optimization

    Foundations and Methodologies

    Authors:
    • Liang Feng
    • Abhishek Gupta
    • Kay Chen Tan
    • Yew Soon Ong
    • Copyright: 2023

    Available Renditions

    • Hard cover
    • Soft cover
    • eBook
  4. Online Machine Learning

    A Practical Guide with Examples in Python

    Editors:
    • Eva Bartz
    • Thomas Bartz-Beielstein
    • Copyright: 2024

    Available Renditions

    • Hard cover
    • Soft cover
    • eBook