Optimization for Machine Learning (Neural Information Processing series) - Image 1

£44.47

MIT Press Optimization for Machine Learning (Neural Information Processing series)

Name: Optimization for Machine Learning (Neural Information Processing series)
Brand: MIT Press
Price: 44.47 GBP
Availability: InStock

Tan

AI & Machine Learning

It has never been this cheap. We have no record of a lower price.

£44 today · cheaper than every other day in the last 3 months

NEW HERE?

Amazon shows you one price. We show you all of them.

Tosheroon watches Amazon prices so you don't have to. Every product on Amazon has a price history — we make it visible. Set the price you'd actually pay, and we'll email you the second it gets there. No app, no account, one email.

WHAT'S ON THIS PAGE

↓ Price chart
when this has been cheap or pricey

↓ Forecast
where the price is heading next

↓ Statistics
all-time high & low, recent range

↑ Price alert
name your number, we'll email you

Price History & Forecast

Grey patches = out of stock. Cheaper = lower on the chart. Hover for exact prices.

Last 21 days • 21 data points (No recent data available)

Historical

Generating forecast...

Price Distribution

Price distribution over 21 days • 1 price levels

Days at Price

Price Analysis

Most common price: £44 (21 days, 100.0%)

Price range: £44 - £44

Price levels: 1 different prices over 21 days

Description

An up-to-date account of the interplay between optimization and machine learning, accessible to students and researchers in both communities. The interplay between optimization and machine learning is one of the most important developments in modern computational science. Optimization formulations and methods are proving to be vital in designing algorithms to extract essential knowledge from huge volumes of data. Machine learning, however, is not simply a consumer of optimization technology but a rapidly evolving field that is itself generating new optimization ideas. This book captures the state of the art of the interaction between optimization and machine learning in a way that is accessible to researchers in both fields. Optimization approaches have enjoyed prominence in machine learning because of their wide applicability and attractive theoretical properties. The increasing complexity, size, and variety of today's machine learning models call for the reassessment of existing assumptions. This book starts the process of reassessment. It describes the resurgence in novel contexts of established frameworks such as first-order methods, stochastic approximations, convex relaxations, interior-point methods, and proximal methods. It also devotes attention to newer themes such as regularized optimization, robust optimization, gradient and subgradient methods, splitting techniques, and second-order methods. Many of these techniques draw inspiration from other fields, including operations research, theoretical computer science, and subfields of optimization. The book will enrich the ongoing cross-fertilization between the machine learning community and these other fields, and within the broader optimization community.

Product Specifications

Brand: MIT Press
Colour: Tan
Format: paperback
ASIN: 0262537761
Category: Books > Subjects > Computing & Internet > Computer Science > AI & Machine Learning
Domain: Amazon UK
Release Date: 30 September 2011
Listed Since: 15 January 2019

Barcode

No barcode data available

MIT Press Optimization for Machine Learning (Neural Information Processing series)

It has never been this cheap. We have no record of a lower price.

Price History & Forecast

Price Distribution

Price Analysis

Description

Product Specifications

Barcode

Similar Products You Might Like

Optimization and Learning via Stochastic Gradient Search (Princeton Series in Applied Mathematics)

An Introduction to Optimization: With Applications to Machine Learning

Nonlinear Optimization

Nonlinear Optimization in Finite Dimensions: Morse Theory, Chebyshev Approximation, Transversality, Flows, Parametric Aspects: 47 (Nonconvex Optimization and Its Applications, 47)

Nonlinear Optimization in Finite Dimensions: Morse Theory, Chebyshev Approximation, Transversality, Flows, Parametric Aspects: 47 (Nonconvex Optimization and Its Applications, 47)

Classical And Modern Optimization: 0 (Advanced Textbooks In Mathematics)

Optimization for Learning and Control

Convex Analysis and Global Optimization: 22 (Nonconvex Optimization and Its Applications, 22)

Modeling and Optimization: Theory and Applications: MOPTA, Bethlehem, PA, USA, August 2014 Selected Contributions: 147 (Springer Proceedings in Mathematics & Statistics, 147)

Non-convex Optimization for Machine Learning (Foundations and Trends in Machine Learning)

Self–Regularity – A New Paradigm for Primal–Dual Interior–Point Algorithms (Princeton Series in Applied Mathematics)

Practical Optimization Methods : With Mathematical Applications

Classical And Modern Optimization: 0 (Advanced Textbooks In Mathematics)

Optimization Theory and Applications (Advanced Lectures in Mathematics)

Global Optimization: From Theory to Implementation: 84 (Nonconvex Optimization and Its Applications, 84)

Practical Mathematical Optimization: Basic Optimization Theory and Gradient-Based Algorithms: 133 (Springer Optimization and Its Applications, 133)

State of the Art in Global Optimization: Computational Methods and Applications: 7 (Nonconvex Optimization and Its Applications, 7)

Smooth Nonlinear Optimization in Rn: 19 (Nonconvex Optimization and Its Applications, 19)

Optimization Concepts and Applications in Engineering

Optimization in Large Scale Problems: Industry 4.0 and Society 5.0 Applications: 152 (Springer Optimization and Its Applications, 152)

Practical Methods of Optimization 2e

First-order and Stochastic Optimization Methods for Machine Learning (Springer Series in the Data Sciences)

Numerical Nonsmooth Optimization: State of the Art Algorithms

The Basics of Practical Optimization