Human-Computer Interaction and Computational Intelligence: Machine Learning Approaches

Machine learning is a field that was established some years ago with the lofty goal of building computational methods to implement various aspects of teaching, Machine Learning (ML) has changed materials science fields that have been able to generate large sufficient datasets to use appropriate statistical methods, yet databases for many important types of materials remain intact. Machine learning is an emerging technology that has attracted the attention of academics and business leaders, and it is expected to develop in the near term. Wireless nodes are anticipated to have ubiquitous connectivity due to machine learning methods. IML (Interactive Machine Learning) strives to augment subjective experience and intellect by closely combining these abilities with machine computing speed and power. This paper provides a full study and characterization of Interactive Machine Learning from the standpoint of interaction design. We present and explain a functional and structural model of a generalized IML system, as well as solution concepts for developing successful IML interactions.