Books like Artificial Intelligence by Melanie Mitchell

Daniel Kahneman's "Thinking, Fast and Slow" explains how two distinct mental systems shape our judgments and decisions, offering insights into the biases that influence everyday choices.

Roger Penrose argues that human thought is rooted in deep, non-computable aspects of physics, suggesting true intelligence cannot be fully replicated by any machine.

Pedro Domingos unveils the quest for a universal learning algorithm that could extract any knowledge from data, exploring how such a machine—the Master Algorithm—might reshape business, science, and society.

Deep Learning is a comprehensive textbook that equips students and practitioners with the foundational knowledge and practical skills needed to enter the fields of machine learning and deep learning.

A clear, concise guide to the fundamentals of machine learning, the branch of artificial intelligence that designs computer programs capable of learning from data and underpinning modern technologies like recommendation systems, face‑recognition, and autonomous vehicles.

A concise, accessible overview of artificial intelligence compiled from Scientific American articles.

Pierre Baldi and Soren Brunak present the key machine learning approaches and apply them to the computational problems encountered in the analysis of biological data. The book is aimed at two types of researchers and students. First are the biologists and biochemists who need to understand new data-driven algorithms, such as neural networks and hidden Markov models, in the context of biological sequences and their molecular structure and function. Second are those with a primary background in physics, mathematics, statistics, or computer science who need to know more about specific applications in molecular biology.

This book constitutes the refereed proceedings of the 9th International Conference on Machine Learning and Data Mining in Pattern Recognition, MLDM 2013, held in New York, USA in July 2013. The 51 revised full papers presented were carefully reviewed and selected from 212 submissions. The papers cover the topics ranging from theoretical topics for classification, clustering, association rule and pattern mining to specific data mining methods for the different multimedia data types such as image mining, text mining, video mining and web mining.

Jerry Kaplan surveys the sweeping influence of artificial intelligence on society, exploring opportunities, challenges, and ethical dilemmas from warfare to personal privacy.

From Robocop to the Terminator to Eve 8, no image better captures our deepest fears about technology than the cyborg, the person who is both flesh and metal, brain and electronics. But philosopher and cognitive scientist Andy Clark sees it differently. Cyborgs, he writes, are not something tobe feared--we already are cyborgs. In Natural-Born Cyborgs, Clark argues that what makes humans so different from other species is our capacity to fully incorporate tools and supporting cultural practices into our existence. Technology as simple as writing on a sketchpad, as familiar as Google or a cellular phone, and aspotentially revolutionary as mind-extending neural implants--all exploit our brains' astonishingly plastic nature. Our minds are primed to seek out and incorporate non-biological resources, so that we actually think and feel through our best technologies...

Explores the history and ethics of artificial intelligence, weighing the benefits and risks of creating intelligent machines and presenting differing viewpoints on the future of computer-driven learning.

Explore the evolution of artificial intelligence from its earliest concepts to modern applications in smart cars and drones, and examine how AI and neurology intertwine to build thinking machines.

The future is here: Self-driving cars are on the streets, an algorithm gives you movie and TV recommendations, IBM's Watson triumphed on Jeopardy over puny human brains, computer programs can be trained to play Atari games. But how do all these thingswork? In this book, Sean Gerrish offers an engaging and accessible overview of the breakthroughs in artificial intelligence and machine learning that have made today's machines so smart. Gerrish outlines some of the key ideas that enable intelligent machines to perceive and interact with the world. He describes the software architecture that allows self-driving cars to stay on the road and to navigate crowded urban environments; the million-dollar Netflix competition for a better recommendation engine (which had an unexpected ending); and how programmers trained computers to perform certain behaviors by offering them treats, as if they were training a dog. He explains how artificial neural networks enable computers to perceive the world-and to play Atari video games better than humans. He explains Watson's famous victory on Jeopardy, and he looks at how computers play games, describing AlphaGo and Deep Blue, which beat reigning world champions at the strategy games of Go and chess. Computers have not yet mastered everything, however; Gerrish outlines the difficulties in creating intelligent agents that can successfully play video games like StarCraft that have evaded solution-at least for now.