A01=Nikolaos Halidias
Author_Nikolaos Halidias
Basis
Category=PBF
Category=UMX
Change of Basis
Conditioning
Determinants
Diagonalization
Dimension
Eigenvalues
Eigenvectors
eq_bestseller
eq_computing
eq_isMigrated=1
eq_isMigrated=2
eq_nobargain
eq_non-fiction
forthcoming
Gram-Schmidt Process
Inner Product
Least Squares
Linear Algebra
Linear Transformations
Matrices
Norms
Null Space
Numerical Linear Algebra
Orthogonality
Principal Component Analysis (PCA)
Projections
Python
QR Decomposition
Rank
Similarity
Singular Value Decomposition (SVD)
Spectral Theorem
Vector Spaces
Product details
- ISBN 9789819826988
- Publication Date: 28 Aug 2026
- Publisher: World Scientific Publishing Co Pte Ltd
- Publication City/Country: SG
- Product Form: Hardback
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This book is a compelling and accessible introduction to linear algebra, ideal for learners and educators across mathematics, data science, engineering, and the physical sciences. It begins with core topics such as systems of linear equations, matrix operations, and Row-Reduced Echelon Form (RREF), and builds a coherent pathway toward advanced concepts like eigenvalues, inner product spaces, and singular value decomposition (SVD). With a clear pedagogical structure and consistent notation, the text supports both conceptual understanding and practical application.What sets this textbook apart is its seamless integration of theory and computation. Python (NumPy/SymPy) is used throughout to implement algorithms, visualize geometric ideas, and connect abstract mathematics to real-world problems. Carefully designed TikZ diagrams enhance geometric intuition, while applications such as least squares, data compression, and principal component analysis (PCA) demonstrate the relevance of linear algebra in modern fields. Integrated coding examples and computational tools encourage hands-on exploration and foster algorithmic thinking.Whether you're mastering Gaussian elimination, exploring the Rank-Nullity Theorem, or experimenting with Python notebooks, Linear Algebra with Python and AI offers a unified and forward-looking approach. With its dual emphasis on mathematical rigor and computational practice, this book equips students with the tools to navigate the mathematical foundations of today's data-driven world.
