A classic introduction to artificial intelligence intended to bridge the gap between theory and practice, Principles of Artificial Intelligence describes fundamental AI ideas that underlie applications such as natural language processing, automatic programming, robotics, machine vision, automatic theorem proving, and intelligent data retrieval. Rather than focusing on the subject matter of the applications, the book is organized around general computational concepts involving the kinds of data structures used, the types of operations performed on the data structures, and the properties of the control strategies used. Principles of Artificial Intelligenceevolved from the author's courses and seminars at Stanford University and University of Massachusetts, Amherst, and is suitable for text use in a senior or graduate AI course, or for individual study.
Primarily intended for the undergraduate and postgraduate students of computer science and engineering, this textbook (earlier titled as Artificial Intelligence and Machine Learning), now in its second edition, bridges the gaps in knowledge of the seemingly difficult areas of artificial intelligence. This book promises to provide the most number of case studies and worked-out examples among the books of its genre. The text is written in a highly interactive manner which fulfils the curiosity of any reader. Moreover, the content takes off from the introduction to artificial intelligence, which is followed by explaining about intelligent agents. Various problem-solving strategies, knowledge representation schemes are also included with numerous case studies and applications. Different aspects of learning, nature-inspired learning, along with natural language processing are also explained in depth. The algorithms and pseudo codes for each topic make this book useful for students. Book also throws light into areas like planning, expert system and robotics. Book concludes with futuristic artificial intelligence, which explains the fascinating applications, that the world will witness in coming years. KEY FEATURES • Day-to-day examples and practical representations for deeper understanding of the subject. • Learners can easily implement the AI applications. • Effective and useful case studies and worked-out examples for AI problems. Target Audience • Students of B.E./B.Tech Computer Science Engineering • Students of M.E./M.Tech Computer Science Engineering
Dr.Vemuri Sudarsan Rao, Professor & Head, Department of Computer Science & Engineering, Sri Chaitanya Institute of Technology and Research (SCIT), Khammam, Telangana, India. Mr.A.Satish, Associate Professor, Department of Computer Science & Engineering, Sri Chaitanya Institute of Technology and Research (SCIT), Khammam, Telangana, India. Mr.BBLV Prasad, Associate Professor, Department of Computer Science & Engineering, Sri Chaitanya Institute of Technology and Research (SCIT), Khammam, Telangana, India.
A Textbook of AI: Principles and Applications is an indispensable guide that illuminates the intricate realm of Artificial Intelligence (AI) with a blend of theoretical depth and practical insights. Authored to cater to the needs of students, educators, and professionals, this comprehensive text transcends traditional boundaries to offer a holistic understanding of AI’s core principles and diverse applications. Structured with clarity and precision, the book navigates through the foundational concepts of AI, including machine learning, neural networks, natural language processing, and computer vision. The narrative seamlessly integrates theoretical underpinnings with real-world examples and case studies, providing readers with a robust foundation for applying AI techniques in various domains. What sets this textbook apart is its conscientious approach to the ethical dimensions of AI. In a landscape where ethical considerations are paramount, the book explores the responsible deployment of AI, addressing societal implications and fostering a nuanced understanding of the ethical challenges associated with AI technologies. A Textbook of AI is not merely an academic resource but a practical compass for those navigating the evolving landscape of AI. With its comprehensive coverage, insightful examples, and ethical considerations, this book is poised to be an essential companion for anyone seeking to comprehend, contribute, and ethically apply AI principles in today’s dynamic technological landscape.
This new resource presents the principles and applications in the emerging discipline of Activity-Based Intelligence (ABI). This book will define, clarify, and demystify the tradecraft of ABI by providing concise definitions, clear examples, and thoughtful discussion. Concepts, methods, technologies, and applications of ABI have been developed by and for the intelligence community and in this book you will gain an understanding of ABI principles and be able to apply them to activity based intelligence analysis. The book is intended for intelligence professionals, researchers, intelligence studies, policy makers, government staffers, and industry representatives. This book will help practicing professionals understand ABI and how it can be applied to real-world problems.
In recent years, machine learning has gained a lot of interest. Due to the advances in processor technology and the availability of large amounts of data, machine learning techniques have provided astounding results in areas such as object recognition or natural language processing. New approaches, e.g. deep learning, have provided groundbreaking outcomes in fields such as multimedia mining or voice recognition. Machine learning is now used in virtually every domain and deep learning algorithms are present in many devices such as smartphones, cars, drones, healthcare equipment, or smart home devices. The Internet, cloud computing and the Internet of Things produce a tsunami of data and machine learning provides the methods to effectively analyze the data and discover actionable knowledge. This book describes the most common machine learning techniques such as Bayesian models, support vector machines, decision tree induction, regression analysis, and recurrent and convolutional neural networks. It first gives an introduction into the principles of machine learning. It then covers the basic methods including the mathematical foundations. The biggest part of the book provides common machine learning algorithms and their applications. Finally, the book gives an outlook into some of the future developments and possible new research areas of machine learning and artificial intelligence in general. This book is meant to be an introduction into machine learning. It does not require prior knowledge in this area. It covers some of the basic mathematical principle but intends to be understandable even without a background in mathematics. It can be read chapter wise and intends to be comprehensible, even when not starting in the beginning. Finally, it also intends to be a reference book. Key Features: Describes real world problems that can be solved using Machine Learning Provides methods for directly applying Machine Learning techniques to concrete real world problems Demonstrates how to apply Machine Learning techniques using different frameworks such as TensorFlow, MALLET, R
Ascend AI Processor Architecture and Programming: Principles and Applications of CANN offers in-depth AI applications using Huawei's Ascend chip, presenting and analyzing the unique performance and attributes of this processor. The title introduces the fundamental theory of AI, the software and hardware architecture of the Ascend AI processor, related tools and programming technology, and typical application cases. It demonstrates internal software and hardware design principles, system tools and programming techniques for the processor, laying out the elements of AI programming technology needed by researchers developing AI applications. Chapters cover the theoretical fundamentals of AI and deep learning, the state of the industry, including the current state of Neural Network Processors, deep learning frameworks, and a deep learning compilation framework, the hardware architecture of the Ascend AI processor, programming methods and practices for developing the processor, and finally, detailed case studies on data and algorithms for AI. - Presents the performance and attributes of the Huawei Ascend AI processor - Describes the software and hardware architecture of the Ascend processor - Lays out the elements of AI theory, processor architecture, and AI applications - Provides detailed case studies on data and algorithms for AI - Offers insights into processor architecture and programming to spark new AI applications
The text discusses the core concepts and principles of deep learning in gaming and animation with applications in a single volume. It will be a useful reference text for graduate students, and professionals in diverse areas such as electrical engineering, electronics and communication engineering, computer science, gaming and animation.
Recent advances in computational algorithms, along with the advent of whole slide imaging as a platform for embedding artificial intelligence (AI), are transforming pattern recognition and image interpretation for diagnosis and prognosis. Yet most pathologists have just a passing knowledge of data mining, machine learning, and AI, and little exposure to the vast potential of these powerful new tools for medicine in general and pathology in particular. In Artificial Intelligence and Deep Learning in Pathology, Dr. Stanley Cohen covers the nuts and bolts of all aspects of machine learning, up to and including AI, bringing familiarity and understanding to pathologists at all levels of experience. - Focuses heavily on applications in medicine, especially pathology, making unfamiliar material accessible and avoiding complex mathematics whenever possible. - Covers digital pathology as a platform for primary diagnosis and augmentation via deep learning, whole slide imaging for 2D and 3D analysis, and general principles of image analysis and deep learning. - Discusses and explains recent accomplishments such as algorithms used to diagnose skin cancer from photographs, AI-based platforms developed to identify lesions of the retina, using computer vision to interpret electrocardiograms, identifying mitoses in cancer using learning algorithms vs. signal processing algorithms, and many more.
