Industrial Applications of Machine Learning (Chapman & Hall/CRC Data Mining and Knowledge Discovery Series): Pedro Larrañaga, David Atienza, Javier Diaz-Rozo, Alberto Ogbechie, Carlos Esteban Puerto-Santana, Concha Bielza: 9780815356226: Amazon.com: Books

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Pedro Larrañaga is Full Professor in Computer Science and Artificial Intelligence at the Universidad Politécnica de Madrid (UPM) since 2007, where he co-leads the Computational Intelligence Group. He received the MSc degree in mathematics (statistics) from the University of Valladolid and the PhD degree in computer science from the University of the Basque Country (excellence award). Before moving to UPM, his academic career was developed at the University of the Basque Country (UPV-EHU) at several faculty ranks: Assistant Professor (1985-1998), Associate Professor (1998-2004) and Full Professor (2004-2007). He earned the habilitation qualification for Full Professor in 2003. Professor Larrañaga has served as Expert Manager of Computer Technology area at the Deputy Directorate of research projects of the Spanish Ministry of Science and Innovation (2007-2010).


Guide to High Performance Distributed Computing: Case Studies with Hadoop, Scalding and Spark (Computer Communications and Networks): K.G. Srinivasa, Anil Kumar Muppalla: 9783319134963: Amazon.com: Books

@machinelearnbot

This timely text/reference describes the development and implementation of large-scale distributed processing systems using open source tools and technologies such as Hadoop, Scalding and Spark. Comprehensive in scope, the book presents state-of-the-art material on building high performance distributed computing systems, providing practical guidance and best practices as well as describing theoretical software frameworks. Fulfilling the need for both introductory material for undergraduate students of computer science and detailed discussions for software engineering professionals, this book will aid a broad audience to understand the esoteric aspects of practical high performance computing through its use of solved problems, research case studies and working source code. Srinivasa is Professor and Head of the Department of Computer Science and Engineering at M.S. Ramaiah Institute of Technology (MSRIT), Bangalore, India. His other publications include the Springer title Soft Computing for Data Mining Applications.


Guide to High Performance Distributed Computing: Case Studies with Hadoop, Scalding and Spark (Computer Communications and Networks): K.G. Srinivasa, Anil Kumar Muppalla: 9783319134963: Amazon.com: Books

@machinelearnbot

This timely text/reference describes the development and implementation of large-scale distributed processing systems using open source tools and technologies such as Hadoop, Scalding and Spark. Comprehensive in scope, the book presents state-of-the-art material on building high performance distributed computing systems, providing practical guidance and best practices as well as describing theoretical software frameworks. Fulfilling the need for both introductory material for undergraduate students of computer science and detailed discussions for software engineering professionals, this book will aid a broad audience to understand the esoteric aspects of practical high performance computing through its use of solved problems, research case studies and working source code. Srinivasa is Professor and Head of the Department of Computer Science and Engineering at M.S. Ramaiah Institute of Technology (MSRIT), Bangalore, India. His other publications include the Springer title Soft Computing for Data Mining Applications.


Guide to High Performance Distributed Computing: Case Studies with Hadoop, Scalding and Spark (Computer Communications and Networks): K.G. Srinivasa, Anil Kumar Muppalla: 9783319383477: Amazon.com: Books

@machinelearnbot

This timely text/reference describes the development and implementation of large-scale distributed processing systems using open source tools and technologies such as Hadoop, Scalding and Spark. Comprehensive in scope, the book presents state-of-the-art material on building high performance distributed computing systems, providing practical guidance and best practices as well as describing theoretical software frameworks. Fulfilling the need for both introductory material for undergraduate students of computer science and detailed discussions for software engineering professionals, this book will aid a broad audience to understand the esoteric aspects of practical high performance computing through its use of solved problems, research case studies and working source code. Srinivasa is Professor and Head of the Department of Computer Science and Engineering at M.S. Ramaiah Institute of Technology (MSRIT), Bangalore, India. His other publications include the Springer title Soft Computing for Data Mining Applications.


Business ethics in the age of artificial intelligence and big data

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Amazon's decision to build its HQ2 in Long Island City – and bring as many as 25,000 jobs to the region – has generated a host of reactions, ranging from elation about what it does for the region's economic development to condemnation and cries of crony capitalism. While those issues are debated, the online retailer's presence presents a tremendous opportunity for business, higher education and political leaders to address the real challenges of the new economy as defined by innovation, entrepreneurship and technological change. Advances in artificial intelligence (AI) – that is, machines that can think and learn – analytics, automation and tracking increasingly will be integrated into just about every aspect of business. All of this underscores the importance of re-examining business ethics. We must train the next-generation workforce to understand that ethical leadership and empathy matter.