Linear Algebra and Learning from Data

By Gilbert Strang

Linear Algebra and Learning from Data is a landmark textbook that connects classical linear algebra with modern data science, machine learning, and artificial intelligence. Written by renowned MIT professor Gilbert Strang, this book explains how core mathematical ideas power today’s most important algorithms.

Designed for clarity and real-world relevance, the text bridges theory and practice, showing how vectors, matrices, eigenvalues, and optimization methods form the foundation of regression, neural networks, and large-scale data analysis.

What This Book Does

This book demonstrates how linear algebra is applied directly to data-driven problems. Readers learn not only the mathematics, but also how and why these concepts are used in machine learning models, optimization, and modern AI systems.

Key Features

• Clear and intuitive explanations of core linear algebra concepts
• Direct connections to machine learning and data science applications
• Coverage of regression, classification, optimization, and neural networks
• Real-world data examples and computational insights
• Authored by Gilbert Strang, the world’s most trusted linear algebra educator

Topics Covered

• Vectors, Matrices, and Linear Transformations
• Systems of Linear Equations
• Vector Spaces and Subspaces
• Eigenvalues and Eigenvectors
• Least Squares and Regression Models
• Optimization in Machine Learning
• Neural Networks and Deep Learning
• PCA and Dimensionality Reduction

The definitive bridge between mathematics and modern data science.
Order today from BooksGoat and master linear algebra for AI.