Algorithmic Aspects of Machine Learning

Introduces cutting-edge research on machine learning theory and practice, providing an accessible, modern algorithmic toolkit.

Ankur Moitra (Author)

9781107184589, Cambridge University Press

Hardback, published 27 September 2018

158 pages
23.7 x 15.7 x 1.9 cm, 0.36 kg

'… the challenges to prove simple but unproven claims and delving deeper into the topics makes it a fascinating read … one of the best parts of the book is the introduction to each chapter. They thoroughly motivate the topic of the chapters.' Sarvagya Upadhyay, SIGACT News

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.

1. Introduction
2. Nonnegative matrix factorization
3. Tensor decompositions – algorithms
4. Tensor decompositions – applications
5. Sparse recovery
6. Sparse coding
7. Gaussian mixture models
8. Matrix completion.

Subject Areas: Machine learning [UYQM], Algorithms & data structures [UMB], Probability & statistics [PBT]