Machine learning

'A manual for the 21st-century citizen.

How Understanding Flocks, Schools and Colonies Can Make Us Better at Communicating, Decision Making and Getting Things Done.

This book contains original, peer-reviewed research articles from the 5th International Conference on Recent Trends in Machine Learning, IoT, Smart Cities, and Applications, held in Hyderabad, India on 28-29 March 2024.

Should digital technology be viewed as a new life form, sharing our ecosystem and coevolving with us?

An examination of the implications for society of rapidly advancing artificial intelligence systems, combining a humanities perspective with technical analysis; includes exercises and discussion questions.

A substantially revised fourth edition of a comprehensive textbook, including new coverage of recent advances in deep learning and neural networks.

An introduction to key concepts and techniques in probabilistic machine learning for civil engineering students and professionals; with many step-by-step examples, illustrations, and exercises.

A clear-eyed look at how AI can complement (rather than eliminate) human jobs, with real-world examples from companies that range from Netflix to Walmart.

An argument that—despite dramatic advances in the field—artificial intelligence is nowhere near developing systems that are genuinely intelligent.

An accessible introduction to the artificial intelligence technology that enables computer vision, speech recognition, machine translation, and driverless cars.

Herbert Simon''s classic work on artificial intelligence in the expanded and updated third edition from 1996, with a new introduction by John E.

An authority on creativity introduces us to AI-powered computers that are creating art, literature, and music that may well surpass the creations of humans.

Key concepts, definitions, examples, and historical contexts for understanding smart cities, along with discussions of both drawbacks and benefits of this approach to urban problems.

An accessible synthesis of ethical issues raised by artificial intelligence that moves beyond hype and nightmare scenarios to address concrete questions.

The significantly expanded and updated new edition of a widely used text on reinforcement learning, one of the most active research areas in artificial intelligence.

A project-based guide to the basics of deep learning.

Advances in training models with log-linear structures, with topics including variable selection, the geometry of neural nets, and applications.

How deep learning—from Google Translate to driverless cars to personal cognitive assistants—is changing our lives and transforming every sector of the economy.

A hands-on approach to tasks and techniques in data stream mining and real-time analytics, with examples in MOA, a popular freely available open-source software framework.

An argument that qualitative representations—symbolic representations that carve continuous phenomena into meaningful units—are central to human cognition.

An argument in favor of finding a place for humans (and humanness) in the future digital economy.

A comprehensive survey of artificial intelligence algorithms and programming organization for robot systems, combining theoretical rigor and practical applications.

Everything you''ve always wanted to know about self-driving cars, Netflix recommendations, IBM''s Watson, and video game-playing computer programs.

A new edition of a graduate-level machine learning textbook that focuses on the analysis and theory of algorithms.

A comprehensive introduction to optimization with a focus on practical algorithms for the design of engineering systems.

A guide to understanding the inner workings and outer limits of technology and why we should never assume that computers always get it right.

A concise and self-contained introduction to causal inference, increasingly important in data science and machine learning.

An introduction to a broad range of topics in deep learning, covering mathematical and conceptual background, deep learning techniques used in industry, and research perspectives.

The first textbook to teach students how to build data analytic solutions on large data sets using cloud-based technologies.

How developments in science and technology may enable the emergence of purely digital minds—intelligent machines equal to or greater in power than the human brain.

What artificial intelligence can tell us about the mind and intelligent behavior.

Perspectives from philosophy, psychology religious studies, economics, and law on the possible future of robot-human sexual relationships.

A concise, nontechnical overview of the development of machine translation, including the different approaches, evaluation issues, and major players in the industry.

The idea of technological singularity, and what it would mean if ordinary human intelligence were enhanced or overtaken by artificial intelligence.

A comprehensive introduction to machine learning that uses probabilistic models and inference as a unifying approach.

This textbook presents deep learning models and their healthcare applications.

An overarching framework for comparing and steering complex adaptive systems is developed through understanding the mechanisms that generate their intricate signal/boundary hierarchies.

Students explore the idea that thinking is a form of computation by learning to write simple computer programs for tasks that require thought.

Cutting-edge machine learning principles, practices, and applicationsThis comprehensive textbook explores the theoretical under pinnings of learning and equips readers with the knowledge needed to apply powerful machine learning techniques to solve challenging real-world problems.

Due to its huge success in industry, mechanism design has been one of the central research topics at the interface of economics and computer science.

This comprehensive guide, featuring hand-picked examples of daily use cases, will walk you through the end-to-end predictive model-building cycle using the latest techniques and industry tricks.

John Maeda is one of the world's preeminent thinkers on technology and design, and in How to Speak Machine, he offers a set of simple laws that govern not only the computers of today, but the unimaginable machines of the future.

This guide covers the fundamental technical principles and various business applications of Generative AI for planning, developing, and evaluating AI-driven products.

Humanity's greatest feat is our incredible ability to learn.

This comprehensive guide provides practical strategies for optimizing Generative AI systems to be more sustainable and responsible.

A hands-on guide for professionals to perform various data science tasks in RKey FeaturesExplore the popular R packages for data scienceUse R for efficient data mining, text analytics and feature engineeringBecome a thorough data science professional with the help of hands-on examples and use-cases in RBook DescriptionR is the most widely used programming language, and when used in association with data science, this powerful combination will solve the complexities involved with unstructured datasets in the real world.

Insightful projects to master deep learning and neural network architectures using Python and KerasKey FeaturesExplore deep learning across computer vision, natural language processing (NLP), and image processingDiscover best practices for the training of deep neural networks and their deploymentAccess popular deep learning models as well as widely used neural network architecturesBook DescriptionDeep learning has been gradually revolutionizing every field of artificial intelligence, making application development easier.

Paul Humphreys pioneered philosophical investigations into the methodological revolution begun by computer simulations.

Order affects the results you get: Different orders of presenting material can lead to qualitatively and quantitatively different learning outcomes.