- Publisher: Cambridge University Press
- Online publication date: September 2018
- Print publication year: 2018
- Online ISBN: 9781316882177
- DOI: https://doi.org/10.1017/9781316882177
This book bridges theoretical computer science and machine learning by exploring what the two sides can teach each other. It emphasizes the need for flexible, tractable models that better capture not what makes machine learning hard, but what makes it easy. Theoretical computer scientists will be introduced to important models in machine learning and to the main questions within the field. Machine learning researchers will be introduced to cutting-edge research in an accessible format, and gain familiarity with a modern, algorithmic toolkit, including the method of moments, tensor decompositions and convex programming relaxations. The treatment beyond worst-case analysis is to build a rigorous understanding about the approaches used in practice and to facilitate the discovery of exciting, new ways to solve important long-standing problems.
Tim Roughgarden - Stanford University, California
Avi Wigderson - Institute for Advanced Study, New Jersey
Sanjeev Arora - Princeton University, New Jersey
Avrim Blum - Toyota Technical Institute at Chicago
M. Bona Source: Choice
* Views captured on Cambridge Core between #date#. This data will be updated every 24 hours.
Usage data cannot currently be displayed.