This textbook is a comprehensive, application-driven guide to mastering linear algebra from foundational principles to advanced machine learning applications. Designed for students, researchers, and professionals in AI, data science, and engineering, the book blends mathematical rigor with practical implementation using Python and popular libraries such as NumPy, SciPy, Matplotlib, and scikit-learn.

Starting with vectors and matrices, the text builds toward systems of linear equations, transformations, determinants, eigenvalues, and vector spacesthen extends to orthogonality, matrix factorizations (e.g., SVD, QR, LU), tensors, and optimization. Each concept is introduced with clear geometric intuition, detailed examples, and step-by-step Python code. Chapters include visual illustrations, code outputs, and exercises that reinforce both theoretical understanding and computational skills. Real-world examples show how core concepts underpin algorithms in regression, PCA, image compression, neural networks, and more.

This book is suitable for either beginner aiming to grasp key ML concepts or an advanced learner exploring spectral methods and tensor decompositions, this book serves as a flexible resource, grounded in mathematics, empowered by code.

Integrates theory with Python code for hands-on learning in real machine learning contexts Includes visual explanations to build geometric intuition behind linear algebra concepts Covers foundational to advanced topics, including matrix factorizations (SVD, QR, LU), tensors, PCA, and optimization

Autorentext

Md. Jalil Piran is an Associate Professor in the Department of Computer Science and Engineering at Sejong University, Seoul, South Korea. He received his Ph.D. in Electronics and Information Engineering from Kyung Hee University, South Korea, in 2016, followed by a post-doctoral fellowship at the same institution. His research interests include Artificial Intelligence, Machine Learning, Data Science, Big Data, the Internet of Things (IoT), and Cyber Security. His extensive body of work has been published in top-tier international journals and presented at high-profile conferences.

Inhalt

"Introduction to Linear Algebra for Machine Learning".- "Vectors".- "Matrices".- "Tensors".- "Linear Systems".- Linear Transformations".- "Determinants".- "Eigenvalues and Eigenvectors".- "Vector Spaces and Subspaces".- "Orthogonality".- "Matrix Decompositions: Factorization and SVD".- "Optimization and Gradients".- "Advanced Topics in Linear Algebra for Machine Learning".