This book presents an integrated collection of representative approaches for scaling up machine learning and data mining methods on parallel and distributed computing platforms. Demand for parallelizing learning algorithms is highly task-specific: in some settings it is driven by the enormous dataset sizes, in others by model complexity or by real-time performance requirements. Making task-appropriate algorithm and platform choices for large-scale machine learning requires understanding the benefits, trade-offs and constraints of the available options. Solutions presented in the book cover a range of parallelization platforms from FPGAs and GPUs to multi-core systems and commodity clusters, concurrent programming frameworks including CUDA, MPI, MapReduce and DryadLINQ, and learning settings (supervised, unsupervised, semi-supervised and online learning). Extensive coverage of parallelization of boosted trees, SVMs, spectral clustering, belief propagation and other popular learning algorithms, and deep dives into several applications, make the book equally useful for researchers, students and practitioners.
Ron Bekkerman is a computer engineer and scientist whose experience spans across disciplines from video processing to business intelligence. Currently a senior research scientist at LinkedIn, he previously worked for a number of major companies including Hewlett-Packard and Motorola. Bekkerman's research interests lie primarily in the area of large-scale unsupervised learning. He is the corresponding author of several publications in top-tier venues, such as ICML, KDD, SIGIR, WWW, IJCAI, CVPR, EMNLP and JMLR. Mikhail Bilenko is a researcher in the Machine Learning and Intelligence group at Microsoft Research. His research interests center on machine learning and data mining tasks that arise in the context of large behavioral and textual datasets. Bilenko's recent work has focused on learning algorithms that leverage user behavior to improve online advertising. His papers have been published at KDD, ICML, SIGIR, and WWW among other venues, and he has received best paper awards from SIGIR and KDD. John Langford is a computer scientist working as a senior researcher at Yahoo! Research. Previously, he was affiliated with the Toyota Technological Institute and IBM T. J. Watson Research Center. Langford's work has been published at conferences and in journals including ICML, COLT, NIPS, UAI, KDD, JMLR and MLJ. He received the Pat Goldberg Memorial Best Paper Award, as well as best paper awards from ACM EC and WSDM. He is also the author of the popular machine learning weblog, hunch.net.
Title: Scaling Up Machine Learning: Parallel and Distributed Approaches
Author:
ISBN: 9781108461740
Binding:
Publisher: Cambridge University Press
Publication Date: 2018-03-29
Number of Pages: 491
Weight: 0.8342 kg
'One of the landmark achievements of our time is the ability to extract value from large volumes of data. Engineering and algorithmic developments on this front have gelled substantially in recent years, and are quickly being reduced to practice in widely available, reusable forms. This book provides a broad and timely snapshot of the state of developments in scalable machine learning, which should be of interest to anyone who wishes to understand and extend the state of the art in analyzing data.' Joseph M. Hellerstein, University of California, Berkeley
'This is a book that every machine learning practitioner should keep in their library.' Yoram Singer, Google Inc.
'The contributions in this book run the gamut from frameworks for large-scale learning to parallel algorithms to applications, and contributors include many of the top people in this burgeoning subfield. Overall this book is an invaluable resource for anyone interested in the problem of learning from and working with big datasets.' William W. Cohen, Carnegie Mellon University, Pennsylvania
'This unique, timely book provides a 360 degrees view and understanding of both conceptual and practical issues that arise when implementing leading machine learning algorithms on a wide range of parallel and high-performance computing platforms. It will serve as an indispensable handbook for the practitioner of large-scale data analytics and a guide to dealing with BIG data and making sound choices for efficient applying learning algorithms to them. It can also serve as the basis for an attractive graduate course on parallel/distributed machine learning and data mining.' Joydeep Ghosh, University of Texas
