Delivery included to the United States

Optimization for Data Analysis

Optimization for Data Analysis

Hardback (21 Apr 2022)

Save $5.60

  • RRP $55.52
  • $49.92
Add to basket

Includes delivery to the United States

10+ copies available online - Usually dispatched within 72 hours

Publisher's Synopsis

Optimization techniques are at the core of data science, including data analysis and machine learning. An understanding of basic optimization techniques and their fundamental properties provides important grounding for students, researchers, and practitioners in these areas. This text covers the fundamentals of optimization algorithms in a compact, self-contained way, focusing on the techniques most relevant to data science. An introductory chapter demonstrates that many standard problems in data science can be formulated as optimization problems. Next, many fundamental methods in optimization are described and analyzed, including: gradient and accelerated gradient methods for unconstrained optimization of smooth (especially convex) functions; the stochastic gradient method, a workhorse algorithm in machine learning; the coordinate descent approach; several key algorithms for constrained optimization problems; algorithms for minimizing nonsmooth functions arising in data science; foundations of the analysis of nonsmooth functions and optimization duality; and the back-propagation approach, relevant to neural networks.

About the Publisher

Cambridge University Press

Cambridge University Press dates from 1534 and is part of the University of Cambridge. We further the University's mission by disseminating knowledge in the pursuit of education, learning and research at the highest international levels of excellence.

Book information

ISBN: 9781316518984
Publisher: Cambridge University Press
Imprint: Cambridge University Press
Pub date:
DEWEY: 519.6
DEWEY edition: 23
Language: English
Number of pages: 238
Weight: 454g
Height: 156mm
Width: 236mm
Spine width: 18mm