… To identify opportunities for improvements in control strategies, we develop an approach
based on machine learning and control theory named PROCEDO— PRediction of Control …

… machine learning-based computational decision making. Throughout this paper, various
machine learning algorithms and approaches that are being used for decision making in the …

… Machine learning addresses the question of how to build computers that improve … Recent
progress in machine learning has been driven both by the development of new learning …

… and decision making makes it necessary to understand the system behavior and components
for effective decision making, traditional paradigms of machine learning … , and decision has …

… Machine learning is increasingly integrated into clinical practice… , patient stratification,
treatment decision making, and early … are critical in machine-learning utilization, including the …

… be able to rely on autonomous, learning decision tools without running afoul of legal standards.
Contrary to popular perceptions, machine learning will not lead to a runaway government…

… Machine Learning with Operational Costs (MLOC),” which is an exploratory form of decision
… subsequent task, thus combining machine learning with the decision that is made afterwards…

… The machine learning techniques for decision making produce good results by handling …
in decision making. In this paper, the review gives the understanding that machine learning is …

… using machine learning to predict human decisions (13–19). Although machine learning has
… based on defining classes of machine-learning models that embody constraints based on …

… , it is not true sentient learning and relies on formal statistical analysis for decision-making.
We propose that the distinct differences between human learning and machine learning risk …