Machine Learning by Ethem Alpaydin
A concise overview of machine learning--computer programs that learn from data--which underlies applications that include recommendation systems, face recognition, and driverless cars. Today, machine learning underlies a range of applications we use every day, from product recommendations to voice recognition--as well as some we don't yet use everyday, including driverless cars. It is the basis of the new approach in computing where we do not write programs but collect data; the idea is to learn the algorithms for the tasks automatically from data. As computing devices grow more ubiquitous, a larger part of our lives and work is recorded digitally, and as "Big Data" has gotten bigger, the theory of machine learning--the foundation of efforts to process that data into knowledge--has also advanced. In this book, machine learning expert Ethem Alpaydin offers a concise overview of the subject for the general reader, describing its evolution, explaining important learning algorithms, and presenting example applications. Alpaydin offers an account of how digital technology advanced from number-crunching mainframes to mobile devices, putting today's machine learning boom in context. He describes the basics of machine learning and some applications; the use of machine learning algorithms for pattern recognition; artificial neural networks inspired by the human brain; algorithms that learn associations between instances, with such applications as customer segmentation and learning recommendations; and reinforcement learning, when an autonomous agent learns act so as to maximize reward and minimize penalty. Alpaydin then considers some future directions for machine learning and the new field of "data science," and discusses the ethical and legal implications for data privacy and security.
Call Number: eBook
ISBN: 0262337592
Publication Date: 2016-09-30
Beginning AI bot frameworks : getting started with bot development Manisha Biswas author. 2018
Machine Learning by
A concise overview of machine learning--computer programs that learn from data--which underlies applications that include recommendation systems, face recognition, and driverless cars. Today, machine learning underlies a range of applications we use every day, from product recommendations to voice recognition--as well as some we don't yet use everyday, including driverless cars. It is the basis of the new approach in computing where we do not write programs but collect data; the idea is to learn the algorithms for the tasks automatically from data. As computing devices grow more ubiquitous, a larger part of our lives and work is recorded digitally, and as "Big Data" has gotten bigger, the theory of machine learning--the foundation of efforts to process that data into knowledge--has also advanced. In this book, machine learning expert Ethem Alpaydin offers a concise overview of the subject for the general reader, describing its evolution, explaining important learning algorithms, and presenting example applications. Alpaydin offers an account of how digital technology advanced from number-crunching mainframes to mobile devices, putting today's machine learning boom in context. He describes the basics of machine learning and some applications; the use of machine learning algorithms for pattern recognition; artificial neural networks inspired by the human brain; algorithms that learn associations between instances, with such applications as customer segmentation and learning recommendations; and reinforcement learning, when an autonomous agent learns act so as to maximize reward and minimize penalty. Alpaydin then considers some future directions for machine learning and the new field of "data science," and discusses the ethical and legal implications for data privacy and security.
Confronting Dystopia by
In Confronting Dystopia, a distinguished group of scholars analyze the implications of the ongoing technological revolution for jobs, working conditions, and income. Focusing on the economic and political implications of AI, digital connectivity, and robotics for both the Global North and the Global South, they move beyond diagnostics to seek solutions that offer better lives for all. Their analyses of the challenges of technology are placed against the backdrop of three decades of rapid economic globalization. The two in tandem are producing the daunting challenges that analysts and policymakers must now confront. The conjuncture of recent advances in AI, machine learning, and robotization portends a vast displacement of human labor, argues the editor, Eva Paus. As Confronting Dystopia shows, we are on the eve of--indeed we are already amid--a technological revolution that will impact profoundly the livelihoods of people everywhere in the world. Across a broad and deep set of topics, the contributors explore whether the need for labor will inexorably shrink in the coming decades, how pressure on employment will impact human well-being, and what new institutional arrangements--a new social contract, for example, will be needed to sustain livelihoods. They evaluate such proposals as a basic income, universal social services, and investments that address key global challenges and create new jobs. Contributors: Vandana Chandra, Mignon Duffy, Dieter Ernst, Vincent Ferraro, Martin Ford, Juliana Martinez Franzoni, Irmgard Nubler, Robert Pollin, David Rueda, Diego Sanchez-Ancochea, Guy Standing, Stefan Thewissen
