Collection
Statistical and Machine Learning with Applications
Technological advances, significant algorithmic developments, and novel methods for fundamentals of mathematical representation, artificial intelligence modeling, statistical learning, deep neural networks, and direct applications in a wide range of disciplines.
This volume will include articles demonstrating cutting-edge statistical and computational-inference and reproducible applications in multiple scientific domains. Mathematical theory, statistical modeling, and machine learning algorithms that address modern statistical, computational, data science, and artificial intelligence challenges and report on their utility, validation, and applications.
Editors
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Prof. S. Ejaz Ahmed
Dr. S. Ejaz Ahmed received his PhD in 1987 from Carleton University, Canada. He holds the position of Professor of Math & Statistics at the Brock University in Canada, specializing in gig and high dimensional data, statistical and machine learning, and statistical inference.
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Prof. Ivo D. Dinov
Dr. Ivo D. Dinov earned his PhD in 1998 from Florida State University, USA. He is the Director of the Statistics Online Computational Resource at the University of Michigan and is an expert in mathematical modeling, statistical analysis, computational processing, generative artificial intelligence models, and scientific visualization of large datasets.
