IDEAS home Printed from https://ideas.repec.org/a/vrn/katinf/y2024i1p182-186.html
   My bibliography  Save this article

Theoretical Foundations Of Artificial Intelligence

Author

Listed:
  • Nikolay Nikolov

    (University of Economics - Varna / Department of Informatics, Varna, Bulgaria)

Abstract

This paper explores the theoretical principles underlying artificial intelligence (AI) algorithms. The focus is on the mathematical and algorithmic models that enable machines to acquire, represent, and utilize knowledge. Concepts from linear algebra, probability theory, and statistics, which are critical for understanding machine learning and deep neural networks, are examined. Various learning methods, including supervised, unsupervised, and reinforcement learning, are analyzed. The paper also discusses the role of algorithmic complexity and computational efficiency in the development of scalable AI systems. In conclusion, future directions and research questions related to the theoretical aspects of artificial intelligence are presented.

Suggested Citation

  • Nikolay Nikolov, 2024. "Theoretical Foundations Of Artificial Intelligence," Conferences of the department Informatics, Publishing house Science and Economics Varna, issue 1, pages 182-186.
    • Handle: RePEc:vrn:katinf:y:2024:i:1:p:182-186
    as

    Download full text from publisher

    File URL: https://informatics.ue-varna.bg/ICTBE2024/ICTBE2024_182-186.pdf
    Download Restriction: no
    ---><---

    More about this item

    Keywords

    artificial intelligence; algorithms; machine learning; neural networks;
    All these keywords.

    JEL classification:

    • C8 - Mathematical and Quantitative Methods - - Data Collection and Data Estimation Methodology; Computer Programs

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:vrn:katinf:y:2024:i:1:p:182-186. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    We have no bibliographic references for this item. You can help adding them by using this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Vladimir Sulov (email available below). General contact details of provider: https://edirc.repec.org/data/uevarbg.html .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.