A Matrix Algebra Approach to Artificial Intelligen

  • 11h 17m
  • Xian-Da Zhang
  • Springer
  • 2020

Matrix algebra plays an important role in many core artificial intelligence (AI) areas, including machine learning, neural networks, support vector machines (SVMs) and evolutionary computation. This book offers a comprehensive and in-depth discussion of matrix algebra theory and methods for these four core areas of AI, while also approaching AI from a theoretical matrix algebra perspective.

The book consists of two parts: the first discusses the fundamentals of matrix algebra in detail, while the second focuses on the applications of matrix algebra approaches in AI. Highlighting matrix algebra in graph-based learning and embedding, network embedding, convolutional neural networks and Pareto optimization theory, and discussing recent topics and advances, the book offers a valuable resource for scientists, engineers, and graduate students in various disciplines, including, but not limited to, computer science, mathematics and engineering.

In this Book

  • A Note from the Family of Dr. Zhang
  • List of Notations
  • Basic Matrix Computation
  • Matrix Differential
  • Gradient and Optimization
  • Solution of Linear Systems
  • Eigenvalue Decomposition
  • Machine Learning
  • Neural Networks
  • Support Vector Machines
  • Evolutionary Computation