Computational Intelligence in Machine Learning

The Volume of “Advances in Machine Learning and Data Science - Recent Achievements and Research Directives” constitutes the proceedings of First International Conference on Latest Advances in Machine Learning and Data Science (LAMDA 2017). The 37 regular papers presented in this volume were carefully reviewed and selected from 123 submissions.

A compilation of the latest trends in the field, Computational Intelligence in Medical Imaging: Techniques and Applications explores how intelligent computing can bring enormous benefit to existing technology in medical image processing as well as improve medical imaging research.

These two-volume set IFIP AICT 717 and 718 constitutes the refereed post-conference proceedings of the 7th International Conference on Computational Intelligence in Data Science, ICCIDS 2024, held in Chennai, India, during February 21–23, 2024. The 63 full papers and 9 short papers were presented in these proceedings were carefully reviewed and selected from 259 submissions. The conference papers are organized in topical sections on:

Through this training we are going to learn and apply how the random forest algorithm works

One important robotics problem is “How can one program a robot to perform a task”? Classical robotics solves this problem by manually engineering modules for state estimation, planning, and control. In contrast, robot learning solely relies on black-box models and data. This book shows that these two approaches of classical engineering and black-box machine learning are not mutually exclusive. To solve tasks with robots, one can transfer insights from classical robotics to deep networks and obtain better learning algorithms for robotics and control. To highlight that incorporating existing knowledge as inductive biases in machine learning algorithms improves performance, this book covers different approaches for learning dynamics models and learning robust control policies. The presented algorithms leverage the knowledge of Newtonian Mechanics, Lagrangian Mechanics as well as the Hamilton-Jacobi-Isaacs differential equation as inductive bias and are evaluated on physical robots.