Neural networks represent one of the most significant developments in machine learning, offering computational models inspired by the biological neural networks of animal brains. As specialised models within the broader machine learning paradigm, neural networks have evolved from theoretical constructs to practical tools that drive modern artificial intelligence applications. This
Machine learning (ML), a fundamental pillar of artificial intelligence, equips computational systems with the capacity to derive insights from data and refine their performance autonomously. Its profound influence permeates diverse domains, encompassing medical diagnostics, financial modelling, and autonomous systems. This essay offers a critical examination of the three principal paradigms
The evolution of learning machines, a cornerstone of Artificial Intelligence (AI), represents one of the most transformative developments in modern science and technology. From rudimentary rule-based systems to sophisticated pattern recognition models capable of processing vast datasets, the trajectory of AI reflects both technological innovation and shifting conceptual paradigms. Defining
Machine learning (ML), a subset of AI, focuses on algorithms that enable systems to learn patterns from data and make predictions or decisions. Mitchell (1997, 2) provides a foundational definition: “A computer program is said to learn from experience E with respect to some class of tasks T and performance
