Mathematical Methods in Machine Learning and Data Science

Dear Colleagues,

This Special Issue is devoted to recent advances in the use of mathematical methods in data science and machine learning. It includes a range of topics of concern to scholars applying quantitative, optimization, combinatorial, logical, topological, geometrical, statistical, algebraic, and algorithmic methods to diverse areas of data science and machine learning. Novel methods, new applications, comparative analyses of models, case studies, and state-of-the-art review papers are particularly welcomed.

Mathematical methods have underlain every major advancement in data science and machine learning—from reproducing kernel Hilbert spaces and back-propagation in the beginning, to more recent methods such as random matrices and graph theory. Combined with the enormous amount of available data and computing power, mathematical methods have propelled machine learning to astonishing results, achieving near-human-level performance on many tasks. As a response to the recent advancements, the objective of this Special Issue is to present a collection of notable mathematical and statistical methods in data science and machine learning. We invite scholars from all around the world to contribute to developing a comprehensive collection of papers on this important theme.

Dr. Firuz Kamalov
Prof. Dr. Hana Sulieman
Dr. Ayman Alzaatreh
Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 250 words) can be sent to the Editorial Office for assessment.

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Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2600 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.