Book Series: Mathematical Foundations for Computer Vision, Machine Learning, and Robotics

Professor Jean Gallier
Computer and Information Science Department
School of Engineering and Applied Sciences
University of Pennsylvania
https://www.cis.upenn.edu/~jean/home.html
https://www.cis.upenn.edu/~jean/jean.html
jean@cis.upenn.edu

Jocelyn Quaintance
Department of Computer and Information Science
University of Pennsylvania
jocelynq@seas.upenn.edu

Advisory Board Members

Stephen Boyd (Stanford University)
https://web.stanford.edu/~boyd/

Gerald B Folland (University of Washington)
https://sites.math.washington.edu/~folland/Homepage/index.html

Gilbert Strang (MIT)
https://math.mit.edu/~gs/

Donald Knuth (Stanford University)
https://www-cs-faculty.stanford.edu/~knuth/

Max Welling (AMLAB, University of Amsterdam)
https://staff.fnwi.uva.nl/m.welling/

Many computer science and engineering projects require a strong mathematical background. This series collect and collate in one comprehensive source the algebra, analysis, topology, geometry, and discrete math necessary for research computer science and engineering. The mathematical ideas are presented in clear, yet rigorous, manner that is accessible to graduate students and working researchers. The books in this series contain chapter and sections which apply the abstract mathematics to modern research problems in deep learning, computer vision, and robotics.

• Aim: To collate and disseminate the mathematics which underpins research in computer science and engineering.
• Scope: Real analysis, point set topology, linear algebra, optimization techniques, abstract algebra, Lie groups and Lie algebras, differential geometry, discrete math, graph theory, logic, computability, harmonic analysis, group representations, and convex geometry.

Call for Book Proposals

To contribute to this book series, contact rkronzek@wspc.com.