Critical Learning in Digital Networks (research in Networked Learning)

This ambitious multidisciplinary volume assembles diverse critical-theory approaches to the current and future states of networked learning. Expert contributors expand upon the existing literature by analyzing the ethical aspects of networked learning and the ongoing need for more open, inclusive, and socially engaged educational practice. Chapters explore in depth evolving concepts of real and virtual, the processes of learning in, against, and beyond the internet, and the role of critical pedagogy in improving social conditions. In all, coverage is both realistic and positive about the potential of digital technologies in higher education as well as social and academic challenges on the horizon.

This book compiles leading research on the development of explainable and interpretable machine learning methods in the context of computer vision and machine learning.

Learn digital forensics and be computer forensics investigator / Certificate after completing course / DFMC+ / DFIR 2023

Learn digital forensics and be computer forensics investigator / Certificate after completing course / DFMC+ / DFIR 2023

This book presents the features and advantages offered by complex networks in the machine learning domain. In the first part, an overview on complex networks and network-based machine learning is presented, offering necessary background material. In the second part, we describe in details some specific techniques based on complex networks for supervised, non-supervised, and semi-supervised learning.