"Scaling Up Machine Learning" – the 2012 Cambridge University Press book. Order from Amazon.com

edited by Ron Bekkerman, Misha Bilenko, and John Langford

Our book will serve as a comprehensive reference for application of popular Machine Learning methods to large amounts of data, with a heavy emphasis on parallelization using modern parallel computing frameworks (MPI, MapReduce, CUDA, DryadLINQ etc.). We did our best to assemble an all star cast of chapter contributors, who represent most prominent industrial and academic research labs, actively working in the area of parallel Machine Learning.

List of chapters:

	Ron Bekkerman (LinkedIn), Mikhail Bilenko (MSR), and John Langford (Yahoo). Scaling Up Machine Learning: Introduction
	Biswanath Panda (Google), Joshua Herbach (Google), Sugato Basu (Google), and Roberto Bayardo (Google). MapReduce and its Application to Massively Parallel Learning of Decision Tree Ensembles
	Mihai Budiu (MSR), Dennis Fetterly (MSR), Michael Isard (MSR), Frank McSherry (MSR), and Yuan Yu (MSR). Large-Scale Machine Learning using DryadLINQ
	Edwin Pednault (IBM), Elad Yom-Tov (Yahoo), and Amol Ghoting (IBM). IBM Parallel Machine Learning Toolbox
	Meichun Hsu (HP Labs), Ren Wu (HP Labs), and Bin Zhang (HP Labs). Uniformly Fine-grained Data Parallel Computing for Machine Learning Algorithms
	Edward Chang (Google), Hongjie Bai (Google), Kaihua Zhu (Google), Hao Wang (Google), Jian Li (Google), and Zhihuan Qiu (Google). PSVM: Parallel Support Vector Machines with Incomplete Cholesky Factorization
	Igor Durdanovic (NEC Labs), Eric Cosatto (NEC Labs), Hans Peter Graf (NEC Labs), Srihari Cadambi (NEC Labs), Venkata Jakkula (NEC Labs), Srimat Chakradhar (NEC Labs), and Abhinandan Majumdar (NEC Labs). Massive SVM Parallelization using Hardware Accelerators
	Krysta Svore (MSR) and Christopher Burges (MSR). Large-Scale Learning to Rank using Boosted Decision Trees
	Ramesh Natarajan (IBM) and Edwin Pednault (IBM). The Transform Regression Algorithm
	Joseph Gonzalez (CMU), Yucheng Low (CMU), and Carlos Guestrin (CMU). Parallel Belief Propagation in Factor Graphs
	Arthur Asuncion (UCI), Padhraic Smyth (UCI), Max Welling (UCI), David Newman (UCI), Ian Porteous (Google), and Scott Triglia (UCI). Distributed Gibbs Sampling for Latent Variable Models
	Wen-Yen Chen (Yahoo), Yangqiu Song (Tsinghua U), Hongjie Bai (Google), Chih-Jen Lin (NTU), and Edward Chang (Google). Large-Scale Spectral Clustering with MapReduce and MPI
	Ron Bekkerman (LinkedIn) and Martin Scholz (HP Labs). Parallelizing Information-Theoretic Clustering Methods
	Daniel Hsu (Rutgers), Nikos Karampatziakis (Cornell), John Langford (Yahoo), and Alex Smola (Yahoo). Parallel Online Learning
	Jeff Bilmes (U Washington) and Amarnag Subramanya (Google). Large-Scale Semi-Supervised Learning
	Evan Xiang (HKUST), Nathan Liu (HKUST), and Qiang Yang (HKUST). Distributed Transfer Learning via Cooperative Matrix Factorization
	Jeremy Kubica (Google), Sameer Singh (U Massachusetts), and Daria Sorokina (Yandex). Parallel Large-Scale Feature Selection
	Adam Coates (Stanford), Rajat Raina (Facebook), and Andrew Ng (Stanford). Large-Scale Learning for Vision with GPUs
	Clement Farabet (NYU), Yann LeCun (NYU), Koray Kavukcuoglu (NYU), Berin Martini (Yale), Polina Akselrod (Yale), Selcuk Talay (Yale), and Eugenio Culurciello (Yale). Large-Scale FPGA-Based Convolutional Networks
	Shirish Tatikonda (IBM) and Srinivasan Parthasarathy (Ohio State). Mining Tree Structured Data on Multicore Systems
	Jike Chong (Parasians), Ekaterina Gonina (Berkeley), Kisun You (Seoul NU), and Kurt Keutzer (Berkeley). Scalable Parallelization of Automatic Speech Recognition