We are happy to share a draft of our forthcoming textbook:
Grey Ballard and Tamara G. Kolda
Tensor Decompositions for Data Science
Cambridge University Press
I will post updated versions at https://www.mathsci.ai/tensor-textbook/ as they become available. Please feel free to share this link with interested students and colleagues.
This book is intended for a graduate-level course in a data-science domain such as mathematics, computer science, engineering, statistics, physics, neuroscience, etc. It is written so that it can be used flexibly. It can be adapted for a subunit in a longer class or can stand on its own in a full semester course. We include substantial background material in linear algebra, optimization, and probability and statistics in the hopes of making the contents widely accessible. The book includes links to several real-world datasets to be used as examples for experiments in the book, grounding the material and providing a playground for student experimentation.
The word cloud image is based on the titles of over 8000 articles that have cited the 2009 SIAM Review article: Tensor Decompositions and Application, by Tamara G. Kolda and Brett Bader. Thanks to Kevin Boyack for providing me the open source data for creating this!
The current cover is a placeholder, so stay tuned for a new look. Nevertheless, if you’re interested in how I created the placeholder cover, check the other book I’ve been working on Unlocking LaTeX Graphics.