The most recommended computer science books

The software interface for Apple’s innovative Macintosh was largely (and legally) modeled on system software designed at the Palo Alto < California research center of Xerox, an East Coast photocopy company whose stodgy executives failed to realize the value of the coding breakthroughs they had funded and nurtured in the heart of northern California’s computer cauldron. Before anyone at the top of Xerox realized the enormity of their errors, the company had licensed to Steve Jobs and Apple key software technologies that animated the Macintosh revolution in the 1980s. Hiltzik’s richly detailed and readable history, based on scores of interviews, is the best account of the epic failure of an American corporate icon. Apple and Jobs went on to achieve glory while Xerox ultimately became a zombie company, having missed the greatest industrial wave of the past 75 years. 

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.

This book provides a brief introduction to the concept of algorithms before discussing the limitations of computation. Specifically, Harel explains undecidable problems (that is, problems for which no algorithm exists) and infeasible problems (that is, problems for which only algorithms are known that have an exponential runtime). I like this book (and its splendid title) because of its focus on the limitations of computation. Harel does a marvelous job in explaining two difficult topics about computation. The understanding of any scientific discipline requires the understanding of its limits, and the limits of computation are as significant as they are surprising.

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.

Michael Lopp, or Rands, as he is commonly known online, has been sharing his knowledge as a software manager for years, mainly through his blog. He is one of the most insightful voices about the art of management in a software environment, and even if you are not a manager yourself (and don’t want to become one), will make you understand and better collaborate with your own manager, and be ready when you need to lead a team or understand how it is to work with other humans.

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!

The most important unanswered question in computer science has a huge public relations problem. Back in the 1970s, this question became known as “P=NP?”—and who could write an exciting book about that? Luckily for us, Lance Fortnow can. As one of the world’s foremost experts on P-vs-NP, he takes us on a wild and truly accessible ride through the most important question about computing. I’ve seen many attempts at making “P=NP?” accessible/understandable/intriguing for non-experts. But Fortnow nails it like nobody else, reformulating P-vs-NP as a search for one of the golden tickets in Charlie and the Chocolate Factory. (Which is another one of my favorite books, even though it’s not going to make it onto this list about algorithms.)

This book is about the design of artifacts that are used by humans. It discusses, in particular, how specific features of cognitive artifacts can support or impede their effective use. The physical artifacts discussed in this book provide concrete illustrations for some abstract computer science notions such as types. I have used some of the examples successfully in talks about computer science for the general audience. A focus of this book is on representations, which plays an important role in many areas of computer science. If you enjoy the examples discussed in this book and like to think about representations, then you are thinking like a computer scientist. 

Yes, it’s a textbook, albeit a particularly well-written one. You may already have it on your shelf, if you’ve taken a programming class or two.

I’m way too old to have used CLRS as a textbook, though! For me, it’s an effectively bottomless collection of neat little ideas—an easy-to-describe problem, then a series of increasingly clever ways to solve that problem. How often do I end up using one of those algorithms? Not very often! But every time I read the description of an algorithm, I get a nugget of pure joy from the “aha” moment when I first understand how it works.

One of the most treasured and unusual books in my personal library. It’s an encyclopedia from another world, entirely written in a made-up language. Page after page of haunting and strange illustrations, organized into specific categories and concepts. Sitting with this book transports me back to the time before I could read, when words felt like incomprehensible symbols. Taking the time to puzzle over this book feels like such a valuable experience. It takes me right out of the familiar ways of taking in information and puts me in a state of mind that has to search and consider the juxtaposition of images and ideas in totally new ways. I can’t recommend this book enough.

The previous book is about the most prevalent forms of AI, many of which focus on prediction or classification.

For example, a bank may use an AI system that utilizes data about those applying for a loan to predict whether they're likely to default. The judicial system might use an AI model which takes into account a convicted person's attributes in order to predict whether that person is likely to re-offend. A hospital might observe attributes of cells in order to classify them as cancerous or not.

Judea Pearl, a computer scientist at UCLA, has been in a long-running effort to get those working in AI to focus more on designing systems which could engage in causal reasoning. And in doing so, he's had a major influence on a number of disciplines, including computer science, philosophy, statistics, epidemiology, and the social sciences.

In The Book of Why, Pearl teams up… show more.

Given my belief that Thanos had a point (what science-math-oriented person can’t tremble at the algorithm of over-population?), it’s no surprise I enjoyed an anthology of different takes on mad scientists and what drives them. I got my fix of humorously boastful ego-maniacs, such as Professor Incognito’s itemized “apology” to his girlfriend (with attempted sincerity because of their couple’s therapist). And the variety of these tales exposed me to a bevy of villainy, from a psychologist who uses the “soft” sciences to unleash the insanity within stable scientists because “everyone deserves the opportunity to go mad” to a side-hustling villainy coach whose budget forces her to choose between new tech plating or weapons-grade plutonium. What’s a would-be world conqueror to do? Relatable.

Microsoft president Brad Smith’s thoughtful book is basically about responsibility in the digital age.

Smith takes an approach that is different from most corporate executive books in that he calls out the threats represented by digital technology his company is built on rather than trying to sugarcoat them.

Then he suggests responsible steps that everyone – individuals, public policy, and companies like his – should take to mitigate those threats.

In The Innovators, Walter Isaacson reminds us that “innovation occurs when ripe seeds fall on fertile ground.” Like the earth beneath our feet, we stand on stratum upon stratum of technological innovation, each with unique markers of its age.

Sometimes, the right idea, person, or group of people arrives at the right time to sow the seeds of something new. Isaacson explores how this phenomenon, teaching us how the collaboration of many individuals working across time and space helped usher in the first digital age in a highly readable survey of the main players and events.

As we stand on the brink of a second digital age, we would do well to search our history for lessons before stepping into the future.

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.

Imagine sitting in a room with 50 of the top software architects in the world and have each of them tell you some brief words of advice about being a software architect.

Welcome to “97 Things Every Software Architect Should Know”. Each 2-page spread features a top software architect offering their advice on some aspect of software architecture. From technical skills to soft skills, this book has it all.

This book is a must-read if you are a software architect, or even thinking about becoming one.

Continuing down the engineering part of this mini-curriculum, we have a collection of interesting ideas, each written by a different author, all of them inspiring.

Some of the chapters in this book I have reread more times than I can count, because the ideas are so original and intriguing that my fingers start to tingle.

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.

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.

After more than thirty years of teaching college students, Dr. “Tony” Fredericks put pen to paper to create a practical and solution-based guide for adjunct professors.

First, the reader is guided to establish best teaching practices for this specialized group of students. The author also provides countless tools as he details how to select a textbook and design a syllabus, define first-day teaching expectations, pose effective questions to propel stimulating discussions, and teach diverse populations of students.

Meticulously constructed, the author composes the text in a well-organized and easy-to-read format. As an aside, Dr. Tony Fredericks currently is the author of over one-hundred-seventy books, comprised as adult non-fiction books, children’s books, pre-service teacher books, and teacher resource books. Not too shabby.