Mathematical Methods in Machine Learning and Data Science
A special issue of Mathematics (ISSN 2227-7390). This special issue belongs to the section "Mathematics and Computer Science".
Deadline for manuscript submissions: 31 January 2025 | Viewed by 1661
Special Issue Editors
Interests: operator algebras; machine learning
Special Issues, Collections and Topics in MDPI journals
Interests: statistics; probability and random processes; business mathematics; statistical modeling and data science
Interests: generalized statistical distributions arising from the hazard function; statistical inference of probability models; characterization of distributions; bivariate and multivariate weighted distributions
Special Issue Information
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 100 words) can be sent to the Editorial Office for announcement on this website.
Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Mathematics is an international peer-reviewed open access semimonthly journal published by MDPI.
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.
Keywords
- mathematical methods
- machine learning
- data science
- optimization
- mathematical statistics
- algorithms
- linear algebra
- dimensionality reduction
- topology
- geometry
- logic
- combinatorics
- fuzzy logic
- time series
- regression
- classification
- imbalanced data
- feature selection
