A Classical Approach to Artificial Intelligence PDF Download
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Author: Munesh Chandra Trivedi
Publisher: KHANNA PUBLISHING HOUSE
ISBN: 8190698893
Category : Computers
Languages : en
Pages : 540
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Book Description
There are many books available in the market on the proposed topic but none of them can be termed as comprehensive. Besides, students face many problems in understanding the language of this books. Keeping these points in mind, Artificial Intelligence was prepared, which should be simple enough to comprehend and comprehensive enough to encompass all the topics of different institutions and universities.
Author: Munesh Chandra Trivedi
Publisher: KHANNA PUBLISHING HOUSE
ISBN: 8190698893
Category : Computers
Languages : en
Pages : 540
Get Book Here
Book Description
There are many books available in the market on the proposed topic but none of them can be termed as comprehensive. Besides, students face many problems in understanding the language of this books. Keeping these points in mind, Artificial Intelligence was prepared, which should be simple enough to comprehend and comprehensive enough to encompass all the topics of different institutions and universities.
Author: Marcus Hutter
Publisher: Springer Science & Business Media
ISBN: 3540268774
Category : Computers
Languages : en
Pages : 294
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Book Description
Personal motivation. The dream of creating artificial devices that reach or outperform human inteUigence is an old one. It is also one of the dreams of my youth, which have never left me. What makes this challenge so interesting? A solution would have enormous implications on our society, and there are reasons to believe that the AI problem can be solved in my expected lifetime. So, it's worth sticking to it for a lifetime, even if it takes 30 years or so to reap the benefits. The AI problem. The science of artificial intelligence (AI) may be defined as the construction of intelligent systems and their analysis. A natural definition of a system is anything that has an input and an output stream. Intelligence is more complicated. It can have many faces like creativity, solving prob lems, pattern recognition, classification, learning, induction, deduction, build ing analogies, optimization, surviving in an environment, language processing, and knowledge. A formal definition incorporating every aspect of intelligence, however, seems difficult. Most, if not all known facets of intelligence can be formulated as goal driven or, more precisely, as maximizing some utility func tion. It is, therefore, sufficient to study goal-driven AI; e. g. the (biological) goal of animals and humans is to survive and spread. The goal of AI systems should be to be useful to humans.
Author: Keith Frankish
Publisher: Cambridge University Press
ISBN: 0521871425
Category : Computers
Languages : en
Pages : 367
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Book Description
An authoritative, up-to-date survey of the state of the art in artificial intelligence, written for non-specialists.
Author: Mehryar Mohri
Publisher: MIT Press
ISBN: 0262351366
Category : Computers
Languages : en
Pages : 505
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Book Description
A new edition of a graduate-level machine learning textbook that focuses on the analysis and theory of algorithms. This book is a general introduction to machine learning that can serve as a textbook for graduate students and a reference for researchers. It covers fundamental modern topics in machine learning while providing the theoretical basis and conceptual tools needed for the discussion and justification of algorithms. It also describes several key aspects of the application of these algorithms. The authors aim to present novel theoretical tools and concepts while giving concise proofs even for relatively advanced topics. Foundations of Machine Learning is unique in its focus on the analysis and theory of algorithms. The first four chapters lay the theoretical foundation for what follows; subsequent chapters are mostly self-contained. Topics covered include the Probably Approximately Correct (PAC) learning framework; generalization bounds based on Rademacher complexity and VC-dimension; Support Vector Machines (SVMs); kernel methods; boosting; on-line learning; multi-class classification; ranking; regression; algorithmic stability; dimensionality reduction; learning automata and languages; and reinforcement learning. Each chapter ends with a set of exercises. Appendixes provide additional material including concise probability review. This second edition offers three new chapters, on model selection, maximum entropy models, and conditional entropy models. New material in the appendixes includes a major section on Fenchel duality, expanded coverage of concentration inequalities, and an entirely new entry on information theory. More than half of the exercises are new to this edition.
Author: T. Dartnall
Publisher: Springer Science & Business Media
ISBN: 9401707936
Category : Psychology
Languages : en
Pages : 444
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Book Description
Creativity is one of the least understood aspects of intelligence and is often seen as `intuitive' and not susceptible to rational enquiry. Recently, however, there has been a resurgence of interest in the area, principally in artificial intelligence and cognitive science, but also in psychology, philosophy, computer science, logic, mathematics, sociology, and architecture and design. This volume brings this work together and provides an overview of this rapidly developing field. It addresses a range of issues. Can computers be creative? Can they help us to understand human creativity? How can artificial intelligence (AI) enhance human creativity? How, in particular, can it contribute to the `sciences of the artificial', such as design? Does the new wave of AI (connectionism, geneticism and artificial life) offer more promise in these areas than classical, symbol-handling AI? What would the implications be for AI and cognitive science if computers could not be creative? These issues are explored in five interrelated parts, each of which is introducted and explained by a leading figure in the field. - Prologue (Margaret Boden) - Part I: Foundational Issues (Terry Dartnall) - Part II: Creativity and Cognition (Graeme S. Halford and Robert Levinson) - Part III: Creativity and Connectionism (Chris Thornton) - Part IV: Creativity and Design (John Gero) - Part V: Human Creativity Enhancement (Ernest Edmonds) - Epilogue (Douglas Hofstadter) For researchers in AI, cognitive science, computer science, philosophy, psychology, mathematics, logic, sociology, and architecture and design; and anyone interested in the rapidly growing field of artificial intelligence and creativity.
Author: Kevin P. Murphy
Publisher: MIT Press
ISBN: 0262018020
Category : Computers
Languages : en
Pages : 1102
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Book Description
A comprehensive introduction to machine learning that uses probabilistic models and inference as a unifying approach. Today's Web-enabled deluge of electronic data calls for automated methods of data analysis. Machine learning provides these, developing methods that can automatically detect patterns in data and then use the uncovered patterns to predict future data. This textbook offers a comprehensive and self-contained introduction to the field of machine learning, based on a unified, probabilistic approach. The coverage combines breadth and depth, offering necessary background material on such topics as probability, optimization, and linear algebra as well as discussion of recent developments in the field, including conditional random fields, L1 regularization, and deep learning. The book is written in an informal, accessible style, complete with pseudo-code for the most important algorithms. All topics are copiously illustrated with color images and worked examples drawn from such application domains as biology, text processing, computer vision, and robotics. Rather than providing a cookbook of different heuristic methods, the book stresses a principled model-based approach, often using the language of graphical models to specify models in a concise and intuitive way. Almost all the models described have been implemented in a MATLAB software package—PMTK (probabilistic modeling toolkit)—that is freely available online. The book is suitable for upper-level undergraduates with an introductory-level college math background and beginning graduate students.
Author: Saroj Kaushik
Publisher:
ISBN: 9788131510995
Category :
Languages : en
Pages : 645
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Book Description
Author: Kevin Warwick
Publisher: Routledge
ISBN: 1136629831
Category : Philosophy
Languages : en
Pages : 192
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Book Description
'if AI is outside your field, or you know something of the subject and would like to know more then Artificial Intelligence: The Basics is a brilliant primer.' - Nick Smith, Engineering and Technology Magazine November 2011 Artificial Intelligence: The Basics is a concise and cutting-edge introduction to the fast moving world of AI. The author Kevin Warwick, a pioneer in the field, examines issues of what it means to be man or machine and looks at advances in robotics which have blurred the boundaries. Topics covered include: how intelligence can be defined whether machines can 'think' sensory input in machine systems the nature of consciousness the controversial culturing of human neurons. Exploring issues at the heart of the subject, this book is suitable for anyone interested in AI, and provides an illuminating and accessible introduction to this fascinating subject.
Author: Stuart Russell
Publisher: Createspace Independent Publishing Platform
ISBN: 9781537600314
Category :
Languages : en
Pages : 626
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Book Description
Artificial Intelligence: A Modern Approach offers the most comprehensive, up-to-date introduction to the theory and practice of artificial intelligence. Number one in its field, this textbook is ideal for one or two-semester, undergraduate or graduate-level courses in Artificial Intelligence.
Author: Donald Tveter
Publisher: Wiley-IEEE Computer Society Press
ISBN:
Category : Computers
Languages : en
Pages : 392
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Book Description
This book pays extra attention to the new ideas in AI: neural networking, case based reasoning, and memory based reasoning, while including the important aspects of traditional symbol processing AI. As much as possible, these methods are compared with each other so that the reader will see the advantages and disadvantages of each method. Second, the new and traditional methods are presented as different ways of doing pattern recognition, giving unity to the subject matter. Third, rather than treating AI as just a collection of advanced algorithms, it also looks at the problems involved in producing the kind of general purpose intelligence found in human beings who have to deal with the real world.
