This book covers the field of machine learning, which is the study of algorithms that allow computer programs to automatically improve through experience. The book is intended to support upper level undergraduate and introductory level graduate courses in machine learning.
As one of the most comprehensive machine learning texts around, this book does justice to the field´s incredible richness, but without losing sight of the unifying principles. Peter Flach´s clear, example-based approach begins by discussing how a spam filter works, which gives an immediate introduction to machine learning in action, with a minimum of technical fuss. Flach provides case studies of increasing complexity and variety with well-chosen examples and illustrations throughout. He covers a wide range of logical, geometric and statistical models and state-of-the-art topics such as matrix factorisation and ROC analysis. Particular attention is paid to the central role played by features. The use of established terminology is balanced with the introduction of new and useful concepts, and summaries of relevant background material are provided with pointers for revision if necessary. These features ensure Machine Learning will set a new standard as an introductory textbook.
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.
The goal of machine learning is to program computers to use example data or past experience to solve a given problem. Many successful applications of machine learning exist already, including systems that analyze past sales data to predict customer behavior, optimize robot behavior so that a task can be completed using minimum resources, and extract knowledge from bioinformatics data. Introduction to Machine Learning is a comprehensive textbook on the subject
Step into the world of Ray Kurzweil, the ´´restless genius´´ (Wall Street Journal) and ´´ultimate thinking machine´´ (Forbes), whose predictions for an age in which man and machine are interchangeable are startling, provocative -- and closer to realization than you think. Imagine a world where the difference between man and machine blurs, where the line between humanity and technology fades, and where the soul and the silicon chip unite. This is not science fiction. This is the twenty-first century according to Ray Kurzweil, the inventor of the most innovative and compelling technology of our era. In his inspired hands, life in the new millennium no longer seems daunting. Instead, it promises to be an age in which the marriage of human sensitivity and artificial intelligence fundamentally alters and improves the way we live. More than just a list of predictions, Kurzweil´s prophetic blueprint for the future guides us through the inexorable advances that will result in: computers exceeding the memory capacity and computational ability of the human brain by the year 2020 (with human-level capabilities not far behind); relationships with automated personalities who will be our teachers, companions, and lovers; and information fed straight into our brains along direct neural pathways. Eventually, the distinction between humans and computers will have become sufficiently blurred that when the machines claim to be conscious, we will believe them.
Weighing in from the cutting-edge frontiers of science, today´s most forward-thinking minds explore the rise of ´´machines that think.´´ Stephen Hawking recently made headlines by noting, ´´The development of full artificial intelligence could spell the end of the human race.´´ Others, conversely, have trumpeted a new age of ´´superintelligence´´ in which smart devices will exponentially extend human capacities. No longer just a matter of science-fiction fantasy (2001, Blade Runner, The Terminator, Her, etc.), it is time to seriously consider the reality of intelligent technology, many forms of which are already being integrated into our daily lives. In that spirit, John Brockman, publisher of Edge. org (´´the world´s smartest website´´ - The Guardian), asked the world´s most influential scientists, philosophers, and artists one of today´s most consequential questions: What do you think about machines that think?
Gain practical skills in machine learning for finance, healthcare, and retail. This book uses a hands-on approach by providing case studies from each of these domains: you´ll see examples that demonstrate how to use machine learning as a tool for business enhancement. As a domain expert, you will not only discover how machine learning is used in finance, healthcare, and retail, but also work through practical case studies where machine learning has been implemented. Machine Learning Applications Using Python is divided into three sections, one for each of the domains (healthcare, finance, and retail). Each section starts with an overview of machine learning and key technological advancements in that domain. You´ll then learn more by using case studies on how organizations are changing the game in their chosen markets. This book has practical case studies with Python code and domain-specific innovative ideas for monetizing machine learning. What You Will Learn Discover applied machine learning processes and principles Implement machine learning in areas of healthcare, finance, and retail Avoid the pitfalls of implementing applied machine learning Build Python machine learning examples in the three subject areas Who This Book Is For Data scientists and machine learning professionals.
Key Features: · Example-rich guide · Step-by-step guide · Move from single-machine to massive cluster Readers should have intermediate skills in Java or Scala. No previous machine learning experience is required.
Automatic language translation systems like those used by Google, have been revolutionized by recent advances in the methods used in statistical machine translation. This first textbook on the topic explains these innovations carefully and shows the reader, whether a student or a developer, how to build their own translation system.