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.
Proposes the machine learning tree, the neural network tree and the evolutionary computation tree
Presents the solid matrix algebra theory and methods for machine learning, neural networks, support vector machines and evolutionary computation
Highlights selected topics and advances in machine learning, neural networks and evolutionary computation
Summarizes about 80 AI algorithms so that readers can further understand and implement relevant AI methods.