Librería Portfolio

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.

Covers exciting recent developments in theoretical machine learning
Introduces sophisticated mathematical tools and demonstrates their use in designing and analyzing algorithms
Includes numerous exercises to challenge the reader´s understanding



Table of Contents
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