The most recommended computer science books

Many people have heard of Bitcoin’s “proof of work” (POW) consensus protocol which involves using huge amounts of energy to solve cryptographic problems. Some of us have also heard of “proof of stake”, an alternative to POW now used by the second largest cryptocurrency, Ether. What Joshua’s book taught me was the similarities and linkages between these two different ways of running a blockchain.

By focusing on the economics, not just the technology, of different consensus protocols, we learn about fundamental issues like the “cost of computational trust”, the future of consensus protocols, and indeed the future of cryptocurrencies.

Attending Harvard College gave me the opportunity to collaborate with great programmers in creating the early ARPA-net. But the best course I took was on compiler theory and construction, using an early draft of the material in this book.

Of course I learned how to build a compiler, which I did as my first job after graduating. But more important, I learned that a well-built compiler is a small amount of language-independent code with two major parts.

First the input part that realizes the content of the lexical and grammatical metadata, like today’s LEX and YACC, to turn the program being compiled into a semantic model.

Second the code generator that reads the semantic model and, based on generative model metadata, turns the semantics of the program being compiled to whatever form you want, whether executable code, assembler language, byte code or whatever.

This approach, while indispensable for compilers and… show more.

When I first heard about cryptography at university, it sounded like mathematical magic. After looking into some randomly chosen crypto books, I was discouraged from digging deeper because they were full of math details.

Understanding Cryptography was different. It was aimed at engineering students like me, and it provided me with the most relevant facts necessary for designing secure devices. Since then, it has always been a great reference book for me.

This book is essential in that it follows a systematic and scientific approach to software development, advocating for clarity in expressing algorithms, providing a rigorous framework for designing and reasoning about programs, and, fundamentally, always focusing on formal methods and mathematical techniques to ensure correctness and efficiency in programming code.

Most importantly, the book doesn’t just show you how to prove programs correct, but also teaches how to arrive from a definition to an efficient and correct solution, so I would recommend this to every developer.

It is easier to say what the ‘new, new thing’ is not than to say what it is. It is not necessarily a new invention. It is not necessarily a new idea – most everything has been considered by someone, at some point. The author explains that the new, new thing is a notion, poised to be taken seriously in the marketplace. It is an item that is a tiny push away from general acceptance and when it gets that push, will change the world.

In 1921, Thorsted Veblen predicted that engineers would one day rule the U.S. economy. He argued that the economy was premised on technology and that engineers (in today’s terms, ‘computer science majors’) were the only ones who understood how technology worked. So, inevitably they would use their superior knowledge to seize power from the financiers, captains of industry, and other business elites.

New growth theory… show more.

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. 

This book provides an excellent description of the various kinds of machine learning approaches and asks the question of whether there will be a Master Algorithm, one single (universal) algorithm that learns all kinds of tasks from data. The author, Pedro Domingos, introduces the different approaches to building intelligence and the research tribes exploring them – Symbolists (with its foundations in Philosophy and Logic), Connectionists (foundations in Neuro/Cognitive Science), Evolutionaries (foundations in Evolutionary Biology), Bayesians (statistical foundations), and Analogizers (Psychology). He also introduces some of his own ideas on what the master machine learning algorithm might look like. It’s a really useful primer for those who are not deeply immersed in machine learning but it’s written for readers with at least a basic engineering and computer science background.

I decided to read this book after watching Dr. Pausch’s presentation about his last lecture before his death and the advice he would give on achieving your childhood dreams. The video had gone viral, and it strongly resonated with me.

What I love about this book is it shows how one can live a fulfilling life while facing difficult circumstances. It showed me how one can accomplish one’s dreams in sometimes unexpected ways.

I’ve probably spent more time with this book than with any other technical book. It’s one of those books where you can get as much out of it as you like by revisiting the material at increasing levels of depth. I appreciate the conversational but rigorous tone, the solved examples, the false starts, the intuition that the authors build, and the applications of algorithm design to realistic problems. The Maximum Flow chapter is not to be missed.

Okay, hear me out. Yes, this book was published in 2007. Yes, it’s ostensibly about ActionScript, the coding language in Flash, which no one uses anymore. But you won’t use this book to learn ActionScript or Flash: You’ll use it to learn how to make things move with code, in any language. You’ll skip over the ActionScript-specific parts in favor of the lucid explanations and helpful illustrations. Your visual brain will appreciate seeing how sines, cosines, and tangents are relevant—and necessary!—to make digital things move. (Your heart will wish your brain had paid better attention in trigonometry class years earlier, but hey, no regrets!) The chapters “Trigonometry for Animation” and “Velocity and Acceleration” alone are worth the purchase price.