Books like Pattern Recognition and Machine Learning — picked by fans

I truly believe that this is the book that brought my generation of AI experts into the fold. Despite having studied AI and ML, this book took me by the hand and grounded me in the fundamentals. I love the fact that it covers everything from mathematical basics to industry-level techniques.

Written by the OGs of deep learning, it's an absolute must-read for anyone serious about the field. Highly recommend for students and engineers alike.

I absolutely love Nick Bostrom's book because it dives deep into the fascinating yet daunting future of artificial intelligence, a topic that resonates with my own work. Bostrom's exploration of how superintelligent AI could emerge and the profound risks it poses is both thought-provoking and essential reading for anyone curious about technology's trajectory.

His insights on the challenges of control and alignment really struck a chord with me, as they highlight the importance of designing AI systems that prioritize human values. This book not only raises critical questions but also inspires a sense of urgency to navigate the future responsibly, making it a personal favorite and a vital resource for anyone interested in the intersection of AI and ethics.

While it’s not the newest tech, I love that it covers the essential groundwork that sparked the modern AI revolution. I personally think its perfect for engineers and data scientists. It's also a great precursor to my book, giving you the strong foundation you need before diving into the next wave of AI advancements.

Stuart Russell is one of the best communicators of our time, and this collaboration with Peter Norvig is the bible of AI. In its fourth edition, this book covers everything you need to know about classic AI, also known as predictive or discriminative AI.

If you want to use AI to optimize business processes, inventory levels, pricing, risk profiles, segment markets, build recommendation engines, or do any of the hard work of running a business, this is the book for you. Perhaps the fifth edition will include generative AI, but this book is still great without that.

I think computers don’t think, and this book taught me how to think about that. I admire it in part because it showed me, a professor of philosophy, how to do scientifically informed philosophy. Unlike so many books on the history of thinking about thinking, just the first chapter of this book is clear, accurate, insightful, and exciting. Equally so is Haugeland’s explanation of what a computer is, making an intellectual adventure of theoretical computer science.

Haugeland uses this to make a compelling case for thinking that computers could genuinely reason. And then he does something that we philosophers tend to love; he launches a provocative critique of that claim, contending that computers can’t think because they don’t “give a damn.” Although it’s now three decades old, this is the book to read if you’re curious about artificial intelligence. 

I love Ted Chiang’s short stories. Chiang’s background is in computer science, and he’s drawn to questions concerning the relationship between language, technology, cognition, and the physical universe. 

His stories are fascinating thought experiments: They depict how a change in the medium or format of language transforms meaning and opens new possibilities for what language can be and do, what humans can think and know, and what it means to be a thinking, speaking human against the backdrop of a vast, infinitely complex universe. His stories are often backed by years of detailed research.

When I read Chiang, I find myself entangled in a strong emotional bond with his characters even as I ruminate on larger questions about what it means to be a language-using human. 

Alan Turing’s 1936 paper “On Computable Numbers, with an Application to the Entscheidungsproblem” was foundational to the development of computer science. To this day, Turing machines, the theoretical computational devices imagined in Turing’s paper, are a research cornerstone as they embody the concept of “computable.” If a programming language can implement a Turing machine, then the language is deemed Turing complete and is, therefore, general-purpose enough to implement any algorithm.

Turing’s paper is readable, but Petzold’s book breaks it down in minute detail to explain the nomenclature and meaning behind Turing’s words. I believe all computer science students should study this paper, and you’ll be hard-pressed to find a more thorough review than the one presented in this book.

This is an entertaining and lighter read than my other recommendations about AI. It is specifically about chatbots trying to pass the Turing Test, and ultimately is a witty story of what it means to be human. For anyone who has ever mistaken an answerphone for a person, or a person for an answerphone!

I think this is one of the most honest books I’ve read on AI.

Russell doesn’t perform gratuitous alarm or sell optimism, he reasons. In my own research and in the workshops I teach on creativity and AI, I’ve spent years around people building systems whose objectives I quietly questioned, and Russell gave me the vocabulary I’d been missing.

I love that he treats control not as a constraint on intelligence but as its precondition and preoccupation. It’s the rare AI book I trust.

If ever a subject deserved the sweeping hand of a highly skilled journalist/historian, it’s generative AI and machine learning. The field is shaped by its founders’ idiosyncratic and fascinating personalities.

NYTimes reporter Cade Metz observed many events first-hand. We read about Go Grandmaster Lee Sedol recovering from losing to Google’s AI by mastering the machine’s logic. We see Geoffrey Hinton flying supine because of his back problems, and the origins of Joy Buolamwini’s famous Gender Shades project.

We get the backstory to the most serious issues: like how well can AI developers be trusted to manage risk? As a journalist-historian myself, I deeply appreciate being immersed in contemporary history.