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Exploring the Mathematical Underpinnings of Artificial Intelligence Systems

  • Unique Paper ID: 171392
  • Volume: 11
  • Issue: 7
  • PageNo: 3399-3403
  • Abstract:
  • The integration of artificial intelligence (AI) in mathematics education has garnered significant scholarly interest for its potential to improve learning outcomes and teaching strategies. This synthesis highlights the use of AI-driven differentiated learning models to cater to diverse student needs and explores the impact of AI on achievement and problem-solving through deep learning techniques. Foundational mathematical disciplines essential to AI development include linear algebra for data representation, calculus for function optimization, optimization and gradient descent for minimizing loss functions, and probability and statistics for data analysis and predictive modeling. Additionally, differential equations, transformations, discrete mathematics, and computational theory provide a robust framework that underpins advancements in AI methodologies. These interdisciplinary insights demonstrate AI's transformative role in enhancing both mathematics education and the broader AI field.
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  • ISSN: 2349-6002
  • Volume: 11
  • Issue: 7
  • PageNo: 3399-3403

Exploring the Mathematical Underpinnings of Artificial Intelligence Systems

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Journal no 47859

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