Paper Details

Linear algebra is a critical component of artificial intelligence (AI), providing the mathematical framework necessary for representing, processing, and computing data. This paper examines key linear algebra techniques such as matrix operations, vector transformations, eigenvectors, and singular value decomposition (SVD) that support various AI models. These techniques are essential for performing tasks like dimensionality reduction, data encoding, and optimization, enabling efficient, scalable approaches to complex AI challenges. By investigating their applications in neural networks, support vector machines, and principal component analysis, this paper demonstrates how linear algebra improves model accuracy, decreases computational demands, and strengthens the reliability of AI algorithms. A solid grasp of these core concepts provides valuable insights for researchers and developers working to enhance AI through optimized data handling and better-performing algorithms.