Michael Darling
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View article: Evaluating the Impact of AI-Personalized Learning Systems in Higher Education; Examining how They Affect Academic Performance across Different Age Groups at Kumasi Technical University
Evaluating the Impact of AI-Personalized Learning Systems in Higher Education; Examining how They Affect Academic Performance across Different Age Groups at Kumasi Technical University Open
Revolutionizing education by introducing innovative methods to enhance student experiences has birthed Artificial Intelligence (AI). This article provided an in-depth overview of AI's educative and transformative influence, particularly co…
View article: Preliminary Results for Using Uncertainty and Out-of-distribution Detection to Identify Unreliable Predictions.
Preliminary Results for Using Uncertainty and Out-of-distribution Detection to Identify Unreliable Predictions. Open
As machine learning (ML) models are deployed into an ever-diversifying set of application spaces, ranging from self-driving cars to cybersecurity to climate modeling, the need to carefully evaluate model credibility becomes increasingly im…
View article: A Decision Theoretic Approach To Optimizing Machine Learning Decisions with Prediction Uncertainty
A Decision Theoretic Approach To Optimizing Machine Learning Decisions with Prediction Uncertainty Open
While the use of machine learning (ML) classifiers is widespread, their output is often not part of any follow-on decision-making process. To illustrate, consider the scenario where we have developed and trained an ML classifier to find ma…
View article: Decision Science for Machine Learning (DeSciML)
Decision Science for Machine Learning (DeSciML) Open
The increasing use of machine learning (ML) models to support high-consequence decision making drives a need to increase the rigor of ML-based decision making. Critical problems ranging from climate change to nonproliferation monitoring re…
View article: Preliminary Results on Applying Nonparametric Clustering and Bayesian Consensus Clustering Methods to Multimodal Data
Preliminary Results on Applying Nonparametric Clustering and Bayesian Consensus Clustering Methods to Multimodal Data Open
In this report, we present preliminary research into nonparametric clustering methods for multi-source imagery data and quantifying the performance of these models. In many domain areas, data sets do not necessarily follow well-defined and…
View article: Quantifying Uncertainty to Improve Decision Making in Machine Learning
Quantifying Uncertainty to Improve Decision Making in Machine Learning Open
Data-driven modeling, including machine learning methods, continue to play an increasing role in society. Data-driven methods impact decision making for applications ranging from everyday determinations about which news people see and cont…
View article: Toward Uncertainty Quantification for Supervised Classification
Toward Uncertainty Quantification for Supervised Classification Open
Our goal is to develop a general theoretical basis for quantifying uncertainty in supervised machine learning models. Current machine learning accuracy-based validation metrics indicate how well a classifier performs on a given data set as…