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Full Description
This book presents a concise exposition of modern mathematical concepts, models and methods with applications in computer graphics, vision and machine learning. The compendium is organized in four parts — Algebra, Geometry, Topology, and Applications. One of the features is a unique treatment of tensor and manifold topics to make them easier for the students. All proofs are omitted to give an emphasis on the exposition of the concepts. Effort is made to help students to build intuition and avoid parrot-like learning.There is minimal inter-chapter dependency. Each chapter can be used as an independent crash course and the reader can start reading from any chapter — almost. This book is intended for upper level undergraduate students, graduate students and researchers in computer graphics, geometric modeling, computer vision, pattern recognition and machine learning. It can be used as a reference book, or a textbook for a selected topics course with the instructor's choice of any of the topics.
Contents
Algebra: Abstract Algebra; Linear Algebra; Tensor Algebra; Exterior Algebra; Geometric Algebra; Boolean Algebra; Geometry: Projective Geometry; Affine Geometry; Differential Geometry; Riemannian Geometry; Non-Euclidean Geometry; Topology: General Topology; Manifolds; Hilbert Spaces; Measure Spaces; Applications: Color Spaces; Projective Geometry and Photography; Tensor Product Spline Surfaces; Image Algebra; Manifold Learning; Support Vector Machines.
