LINEAR ALGEBRA TO ARTIFICIAL INTELLIGENCE AND ITS APPLICATIONS

ABSTRACT

Linear algebra plays a pivotal role in the field of artificial intelligence (AI), servingas a foundational framework for numerous algorithms and techniques. Thisstudy VII aims to explore the application of linear algebra in AI, examining its significanceinvarious aspects of AI research and development. Beginning with an overviewoffundamental concepts in linear algebra, such as vectors, matrices, andlineartransformations, the study delves into specific applications of these concepts inAI. One key area of focus is machine learning, where linear algebra forms the backboneof algorithms for tasks such as regression analysis, and principal component analysisfor dimensionality reduction. Beyond machine learning, linear algebra finds applications in computer vision,natural language processing, robotics, and other subfields of AI. Techniques suchasimage processing, feature extraction, and semantic analysis rely heavilyonlinearalgebra operations for data manipulation and interpretation. Furthermore, this study considers the role of linear algebra in optimizationproblems,where techniques such as gradient descent and linear programming are utilizedtooptimize objective functions and constraints in AI algorithms. Through a comprehensive review of literature and case studies, this studyprovidesinsights into the practical significance of linear algebra in AI researchanddevelopment. By understanding the foundational principles of linear algebraanditsapplications in AI, researchers and practitioners can leverage its power toadvancethe capabilities and effectiveness of AI systems.