Mathematics Behind Machine Learning

Dear Colleagues,

Machine learning processes have been extensively used to accurately determine the hyperparameters of many optimization processes to estimate the parameters of complex mathematical models representing real processes. The mathematical modeling of biological, chemical, medical, electrical, and electronics phenomena helps to interpret the available data to make optimal decisions. Many mathematical foundations used in machine learning are fundamental and sometimes prioritized over heuristic or metaheuristics schemes when the problem’s formal conditions are available. This Special Issue on “Mathematics behind Machine Learning” is focused on detecting important contributions on mathematical modeling, parameter estimation, optimization processes using deterministic approaches or decision schemes based on probabilistic decision related to machine learning methods finding potential applications in areas such as microgrids, biomedical image analysis, signal and image processing, sensors and measurements, robotics, controller design, and energy renewable sources, among others.

Dr. Juan Gabriel Avina-Cervantes
Guest Editor

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.

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. Axioms is an international peer-reviewed open access monthly 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 2400 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.