Information Theory for Data Science, AI and Machine Learning
A special issue of Entropy (ISSN 1099-4300). This special issue belongs to the section "Information Theory, Probability and Statistics".
Deadline for manuscript submissions: 30 April 2026 | Viewed by 2
Special Issue Editors
2. Laboratory of AI Business and Data Analytics (LAMBDA), Tel Aviv University, Ramat-Aviv 69978, Israel
Interests: AI; machine learning; data science; business analytics; information theory; complex systems
Special Issues, Collections and Topics in MDPI journals
Interests: statistical learning; predictive modeling; inference problems; information theory and learning; data analysis and applications
Special Issues, Collections and Topics in MDPI journals
Special Issue Information
Dear Colleagues,
Data science, artificial intelligence (AI), and machine learning are rapidly evolving fields that focus on extracting insights and making decisions based on complex, high-dimensional, and often noisy data. These disciplines draw from a variety of foundations—data engineering, data visualization, predictive analytics, data mining, machine learning, statistics, and increasingly, information theory—to support performance guarantees, robustness, fairness, explainability, and interpretability.
In recent years, there has been growing interest in the application of information theoretic tools to better understand and advance core challenges in data science, AI, and machine learning. Information theory offers a principled framework for analyzing uncertainty, complexity, generalization, data efficiency, and communication, all of which are central to modern algorithmic systems.
This Special Issue invites original and unpublished contributions that explore theoretical foundations, novel methodologies, and practical implementations of information theory in the context of data science, AI, and machine learning. We particularly encourage work that bridges theory and practice and demonstrates how information theoretic insights can inform the design, analysis, and deployment of intelligent systems.
Prof. Dr. Irad Ben-Gal
Dr. Amichai Painsky
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. Entropy 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 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
- data science
- machine learning
- AI
- statistics and inference
- information theory
- federated learning
- reinforcement learning
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