Computer Science and Engineering
this wiki
Fundamentals Edit
In order to have a solid CS&E foundation, you should touch upon each of the following fundamental topics. If your focus is on CpE or ECE or have a strong interest in hardware then you should also study the EEE Fundamentals.
Basic Programming & Data Structures Edit
Prerequisites: Grade School Algebra. Useful tangential knowledge: Logic or Proofs.
Besides considering what are good books for teaching programming concepts, you also must pick a particular language to start with. Don't start learning too many languages at once before you have a solid grasp of one, say C++, to act as a frame of reference. As for language choice, you should consider avoiding Java and Basic like the plague as they can instill terrible habits and don't listen to rabid C fanboys that claim C++ is "too hard for beginners", "bloated", "slow", or any other incorrect and greatly misinformed claims on the C++ language. You should also be aware that most material in this list and in general will assume that you know or are familiar with C++ or at least C.
Possible books to look into if you want to start with C++ (which is arguably the most versatile) would be:
 Programming: Principles and Practice Using C++ by Stroustrup (Written by the creator of C++. An excellent introduction to programming and to C++)
 C++ Primer by Lippman, Lajoie, and Moo (Works as a follow up to Programming: Principles and Practice or as a first book on C++ with some prior programming experience) *Not to be confused with C++ Primer Plus (Stephen Prata), which has a less than favorable reception.
This cannot be stressed enough: Even if all you were looking for is to learn the basics of how to code, once you've mastered the syntax of programming (such as with the books above) you mustn't stop there. Rather, continue on to studying the structure, implementation, and analysis of common data structures (and the basic algorithms that go with them). This is utterly essential for fully grasping programming and for coding any program with even an ounce of complexity behind it (i.e. any useful program whatsoever). You do not know any programming until you've done so.
The best data structures book for C++ is:
 Data Structures and Algorithms in C++ by Drozdek
For additional references on advanced topics in C++ programming:
 The C++ Standard Library: A Tutorial and Reference by Josuttis
 Effective C++: 55 Specific Ways to Improve Your Programs and Designs by Scott Meyers
 Effective Modern C++: 42 Specific Ways to Improve Your Use of C++11 and C++14 by Scott Meyers
 The C++ Programming Language by Stroustrup
Other language materials can be found on the Programming Textbook Recommendations page
Learn your way around an Unix shell, Make, System Programming and C Edit
Assuming you don't know them already that is. If you know the basics of C++, learning C distills down to learning what you can't do anymore and the few quirks where C behaves differently: see C for C++ Programmers for some of the differences.
 Advanced Programming in the UNIX Environment by Stevens and Rago (The rough Windows equivalent would be Windows System Programming by Hart and/or Windows Via C/C++ by Richter and Nasarre)
 Make Manual
 The C Programming Language by Kernighan and Ritchie (known as K&R, but beware it was published in 1988 and the C language has changed with C99 and C11 standards)
 C Programming: A Modern Approach by King
You should also start learning how to use revision control systems like SVN or git especially if you see yourself working on large code bases or on a team in the future. Contrary to the popular belief, learning to use a 1970s style terminal text editor like vim/emacs is completely unnecessary and unhelpful.
Computer Architecture and Digital Logic Edit
Digital Logic Edit
Prerequisites: Precalculus. Useful tangential knowledge: Programming and Circuits.
 Digital Design: Principles and Practices by Wakerly
 Fundamentals of Logic Design by Roth & Kinney (Alternative to Wakerly)
Computer Organization and Architecture Edit
Prerequisites: Programming. Useful tangential knowledge: Unix, Circuits, and Logic.
 Computer Organization and Design: The Hardware/Software Interface by Patterson & Hennessy (The assembly language used in old editions was MIPS, the newest edition uses ARM)
 Computer Systems: A Programmer's Perspective by Bryant & O'Hallaron (AMD64 based assembly)
Follow up 2nd book on advanced modern and high performance architecture (for the CpEs and EEs):
 Computer Architecture: A Quantitative Approach by Hennessy & Patterson (The order of their names differentiates between their 2 books, this one is more advanced)
 Parallel Computer Organization and Design by Dubois, Annavaram, and Stenström (Covers more than just parallel topics)
For a comprehensive reference of x86/AMD64 assembly language: Intel^{®} 64 and IA32 Architectures Software Developer Manuals (It's 3463 pages long so do not try to read it all)
Operating Systems Edit
Prerequisites: Architecture and C/C++ Programming. Useful tangential knowledge: Unix, System Programming.
 Operating System Concepts by Silberschatz, Galvin, and Gagne (The Dinosaur book)
 Modern Operating Systems by Tanenbaum
For more see the OS Development section
Mathematics Primer Edit
To study algorithms, compilers, complexity theory, and advanced topics you'll need some familiarity with abstract topics such as proofs, sets, number theory, combinatorics, graph theory, and probability. But the good (or bad) news is that you don't need that much in terms of depth in most of these areas at the beginning of your studies so you don't have to worry about fully mastering them all at once. Eventually, you will want to dive deeper and resources for that are provided in the Mathematics section. Now is also the best time for you to consider learning LaTeX and practicing typesetting most of your work in it.
Proofs and Mathematical Reasoning Edit
Prerequisites: Precalculus. Useful tangential knowledge: Digital Logic or Philosophical Logic.
The most important topics you absolutely want to fully grasp here is the skill of reading and writing proofs, logical expressions, and naive set theory. Sadly, even majors who take courses on discrete mathematics still find that proofs totally elude them. You could try to pick up proofs in a discrete math book but you will find yourself lacking in much needed practice. Therefore it's strongly recommend that you study a mathematics oriented exposition on proofs instead. The last thing you want is to do is to be struggling with proofs when you move on to later topics and that will almost guarantee you failure or at least a terrible time.
 A Transition to Advanced Mathematics by Smith, Eggen, and St. Andre
 A Primer of Abstract Mathematics by Ash
 Conjecture and Proof by Laczkovich (An excellent supplement to the above books and shows a larger variety of proofs in mathematics)
 Proofs from THE BOOK by Aigner and Ziegler (Not a textbook on proofs but it is an excellent collection of well done and elegant proofs to appreciate and draw inspiration from)
If you still find yourself struggling with proofs, then the following books take a far more hand holding approach through them (but at the cost of excluding some valuable material)
 How to Prove It: A Structured Approach by Velleman
 How to Read and Do Proofs: An Introduction to Mathematical Thought Processes by Solow
 Book of Proof by Hammack
Now that you can finally reason your way out of a paper bag, there's not much to learn that you couldn't pick up as you go. But to be familiar with the topics ahead of time, these books serve as a crash course (remember that discrete mathematics barely scratches the surface of most topics they cover, feel free to skip to books covering these topics if you want depth):
 Concrete Mathematics: A Foundation for Computer Science by Graham, Knuth, and Patashnik
 Discrete Mathematics and Its Applications by Rosen (The level is a bit lower than Graham and covers similar material to the proof books)
Probability Edit
There's also the standard requirements that you know Calculus and Linear Algebra so if you haven't already done so, go learn about them. One last thing to study at this level is introductory probability which is indispensable for dealing with the real world.
 The Art Of Probability by Hamming (Great introduction or supplement to the other probability texts)
 Probability by Pitman
 A First Course in Probability by Ross
 An Introduction to Probability and Random Processes by Rota and Baclawski
See also the EEE's Probability and Stochastic Processes recommendations.
Algorithms Edit
Prerequisites: Programming and Proofs. Useful tangential knowledge: Graph theory, Combinatorics
The study of algorithms and their analysis is essential for any serious work in the field.
 Introduction to Algorithms by Cormen, Leiserson, Rivest, and Stein ^{[Errata]} (Known as CLRS and is very encyclopedic)
 Algorithms in C++ Parts 15: Fundamentals, Data Structures, Sorting, Searching, and Graph Algorithms by Sedgewick (Also available in a C version. Covers theory and implementation details)
 Algorithm Design by Kleinberg and Tardos (Greater focus on the process of designing algorithms rather than collecting and analyzing the most common algorithms)
 The Design and Analysis of Algorithms by Kozen (Supplement to the above books with more advanced topics and good introduction to complexity theory)
 An Introduction to the Analysis of Algorithms by Sedgewick and Flajolet (The book concerns itself with the mathematical analysis of algorithms. The authors' "Analytic Combinatorics" book is a continuation)
and as a reference
 The Art of Computer Programming by Knuth
Various Programming Languages, Paradigms, and Compilers Edit
Prerequisites: Programming. Useful tangential knowledge: Architecture and Algorithms.
You should study a few different "feeling" programming languages that operate differently from what you're comfortable with. Common languages people tend to study are: Lisp/Scheme/Racket, Prolog, Haskell, Forth (or Factor), J, Matlab, Python, Lua, C#, and C++. Similarly you should also delve into the study of the structures that these languages have and into the theory of compilers behind their translation into machine instructions
Prerequisites: Architecture and Algorithms. Useful tangential knowledge: Automata, Complexity Theory, and Mathematical Logic.
 Programming Language Pragmatics by Scot
 Engineering a Compiler by Cooper and Torczon ^{[Errata]}
 Compilers: Principles, Techniques, and Tools by Aho, Lam, Sethi, and Ullman (The Dragon book)
 Advanced Compiler Design and Implementation by Muchnick (More advanced, read it when you finish the above and still want more)
Automata, Computability Theory, and Complexity Theory Edit
Prerequisites: Algorithms. Useful tangential knowledge: Digital Logic, Architecture, and Mathematical Logic.
"How do you know that it's even possible to solve a given problem on a computer? And even if it is possible, how difficult in terms of computational resources will it be to solve that problem?" The Theory of Computation is based on answering these fundamental questions. The subject naturally breaks down into 3 distinct parts. First, we must come up with mathematical models of what computation devices are so we can start proving general theorems and results about them. This is the domain of Formal Languages and Automata. Next comes Computability Theory where we determine what's possible to do on these abstract machines. Finally, we reach Complexity Theory which concerns itself with what is possible given limited computational resources: "Can a problem be solve in logarithmic or polynomial space or in polynomial or exponential or double exponential time? Does randomness help you solve problems faster? Is finding the negative answer as easy as finding the positive? Is the problem parallelizable?"
 Introduction to the Theory of Computation by Sipser ^{[Errata]}
Sipser is a very easy to read (almost middle school level) book covering all three areas while requiring no more than the ability to read and write simple proofs. Great for people outside of CS who want to learn and understand the subject with the added benefit that anyone who completes the book will know the subject better than 99.95% of CS majors and will be able to easily call them out when they butcher and grossly misrepresent it (which they do quite often). Downside is that it's horribly overpriced and math savvy readers will be annoyed that it doesn't go much deeper.
 Automata and Computability by Kozen (Covers the first 2 areas of the subject in more mathematical detail than Sipser)
 Computational Complexity: A Modern Approach by Arora and Barak (Can be used as a follow up to Sipser or 1st book on Complexity that goes deep)
 Theory of Computation by Kozen
References
 Computers and Intractability: A Guide to the Theory of NPCompleteness by Garey and Johnson
Special Topics Edit
Parallel Programming Edit
Prerequisites: Programming in C/C++ and Architecture. Useful tangential knowledge: Operating Systems.
As computers grow increasingly parallel, it's important to learn how to (and when to) program with OpenMP, MPI, pthreads/std::thread, and OpenCl and be aware of the unique aspects of parallel algorithms from their linear brothers. Good books are hard to find but most recommend these as a general introduction:
 An Introduction to Parallel Programming by Pacheco (covers MPI, Pthreads, and OpenMP)
 Introduction to Parallel Computing by Grama, Karypis, Kumar, and Gupta (covers MPI, Pthreads, and OpenMP)
 C++ Concurrency in Action: Practical Multithreading by Williams (just covers std::thread)
 Heterogeneous Computing with OpenCL by Gaster, Howes, Kaeli, et al. (you should be familiar with basic parallel programming before moving on to GPGPU coding)
 OpenCL in Action: How to Accelerate Graphics and Computations by Scarpino
 OpenCL Programming Guide by Munshi, Gaster, et al.
Networks Edit
Prerequisites: Programming and Probability. Useful tangential knowledge: Operating Systems, Algorithm, Parallel Programming, or Graph Theory
 Computer Networks: A Systems Approach by Peterson and Davie
 Computer Networks by Tanenbaum
 Unix Network Programming, Volume 1: The Sockets Networking API by Stevens, Fenner, and Rudoff
 Interconnections: Bridges, Routers, Switches, and Internetworking Protocols by Perlman
 Data Networks by Bertsekas and Gallager
Computer Security and Cryptography Edit
Prerequisites: Proofs, (lite) Probability, and (lite) Algorithms/Programming. Useful tangential knowledge: Complexity Theory, Abstract Algebra, and Number Theory
 Introduction to Modern Cryptography by Katz and Lindell (Great starting point, focuses on provable security that answers the question of "when you should use what system and why")
 Cryptography Engineering: Design Principles and Practical Applications by Niels Ferguson, Bruce Schneier, Tadayoshi Kohno (Focuses on implementation details of cryptographic systems)
 An Introduction to Mathematical Cryptography by Hoffstein, Pipher, and Silverman
Also see Reverse Engineering and Malware Analysis
Information Theory and Coding Theory Edit
Prerequisites: Proofs, Probability, and Linear Algebra. Useful knowledge: Abstract Algebra, Analysis, and Measure Theory. Useful tangential knowledge: Signal and Systems Analysis, Digital Signal Processing, Communication Systems, and Complexity Theory
 Elements of Information Theory by Cover and Thomas
 The Mathematical Theory of Communication by Claude Shannon and Warren Weaver (The paper that started it all and is very readable, beautiful, and still useful to read)
 Principles of Digital Communication and Coding by Viterbi and Omura
 Introduction to Data Compression by Sayood
 Information Theory by Ash
 Network Information Theory by El Gamal and Kim
 Coding and Information Theory by Roman
 Information Theory and Reliable Communication by Gallagher
Also take a look at MacKay's book in the next section below.
AI, Machine Learning, and Computer Vision Edit
Prerequisites: Programming Languages, Probability, Vector Calculus, and Linear Algebra. Useful knowledge: Statistics (especially Bayesian), Graph Theory, Optimization, Approximation Algorithms, Information Theory, Fourier and Functional Analysis, and Measure Theory. Useful tangential knowledge: Signal and System Analysis, Digital Signal Processing, Control Theory, Theoretical Neuroscience
Warning: most everything people say about these areas are wild pipe dreams, don't get your hopes up. Studying digital image processing and a bit of computer graphics beforehand would be very helpful for computer vision.
 Artificial Intelligence: A Modern Approach by Russell and Norvig
 Computer Vision by Shapiro and Stockman
 Multiple View Geometry in Computer Vision by Hartley and Zisserman ^{[Errata]}
 Computer Vision: Algorithms and Applications by Szeliski
 Pattern Recognition and Machine Learning by Bishop
 Information Theory, Inference & Learning Algorithms by MacKay (Available for free online)
Natural Language Processing Edit
 Natural Language Understanding by Allen (a bit dated)
 Foundations of Statistical Natural Language Processing by Manning and Schütze
 Speech and Language Processing: An Introduction to Natural Language Processing, Computational Linguistics, and Speech Recognition by Jurafsky and Martin
Computer Graphics and Image Processing Edit
Prerequisites: Programming, Vector Calculus, and Linear Algebra. Useful tangential knowledge: Modern Geometry, Quaternions, Signal and System Analysis, Numerical Analysis, or Parallel Programming
 Digital Image Processing by Gonzalez
 Fundamentals of Computer Graphics by Shirley and Marschner
 Computer Graphics: Principles and Practice by Hughes, van Dam, McGuire, Sklar, Foley, Feiner, Akeley (updated/rewritten version of the classic CG bible Computer Graphics: Principles and Practice in C by Foley, van Dam, Feiner, and Hughes)
Discrete and Computational Geometry Edit
 Computational Geometry: Algorithms and Applications by de Berg, Cheong, van Kreveld, and Overmars
 Discrete and Computational Geometry by Devadoss and O'Rourke
 Lectures on Discrete Geometry by Matousek ^{[Errata]}
Advanced Algorithms and Mathematical Optimization Edit
Prerequisites: Algorithms, Probability, Proofs, and Linear Algebra. Useful tangential knowledge: Combinatorics (strongly advised), Graph Theory (strongly advised), Complexity Theory
Linear Programming/Optimization Edit
 Introduction to Linear Optimization by Bertsimas & Tsitsiklis
 Theory of Linear and Integer Programming by Schrijver
Combinatorial Optimization and Network Flows Edit
 Network Flows by Ahuja, Magnanti & Orlin ^{[Errata]}
 Combinatorial Optimization by Cook, Cunningham, Pulleyblank, and Schrijver
 Combinatorial Optimization  Theory and Algorithms by Korte & Vygen
 Combinatorial Optimization, Polyhedra and Efficiency by Schrijver ^{[Errata]}
Convex Optimization Edit
 Convex Optimization by Boyd and Vandenberghe
 Lectures on Modern Convex Optimization: Analysis, Algorithms, and Engineering Applications by BenTal and Nemirovski
 Convex Optimization & Euclidean Distance Geometry by Dattorro
 Convex Analysis by Rockafellar (for the theory, requires some baby analysis)
Approximation Algorithms Edit
 Approximation Algorithms by Vazirani
 The Design of Approximation Algorithms by Williamson and Shmoys
 Design and Analysis of Approximation Algorithms by DingZhu Du, KerI Ko, Xiaodong Hu
Randomized Algorithms Edit
 Randomized Algorithms by Motwani and Raghavan
Numerical Analysis and Methods Edit
Prerequisites: Basic Programming, Vector Calculus, Linear Algebra, basic DEs. Useful tangential knowledge: Algorithms, Architecture, Analysis.
Overviews of Numerical Analysis Edit
 Numerical Methods for Scientists and Engineers (Dover Books) by Hamming
 Numerical Analysis by Burden and Faires
 An Introduction to Numerical Analysis by Atkinson
 An Introduction to Numerical Analysis by Süli and Mayers
 Numerical Analysis: A Mathematical Introduction by Schatzman

 Finite Difference and Spectral Methods for Ordinary and Partial Differential Equations by Trefethen
 Chebyshev and Fourier Spectral Methods (Dover Books on Mathematics) by Boyd
Numerical Linear Algebra Edit
 Matrix Computations by Golub and Van Loan
 Numerical Linear Algebra by Trefethen and Bau III
 Matrix Analysis by Horn and Johnson
Approximation Theory Edit
 Introduction to Approximation Theory by Cheney
 Interpolation and Approximation (Dover Books) by Davis
 Approximation Theory and Methods by Powell
 Approximation Theory and Approximation Practice by Trefethen ^{[site, mirror]}
More advanced books:
 Theory of Approximation (Dover Books) by Achieser
 Theory of Approximation of Functions of a Real Variable (Dover Books) by Timan (Careful, Amazon links this and Achieser's book together...)
Numerical Ordinary Differential Equations Edit
 Computer Methods for Ordinary Differential Equations and DifferentialAlgebraic Equations by Ascher and Petzold
 Numerical Methods for Ordinary Differential Equations by Butcher
 Solving Ordinary Differential Equations I: Nonstiff Problems by Hairer, Nørsett, and Wanner
 Solving Ordinary Differential Equations II: Stiff and DifferentialAlgebraic Problems by Hairer and Wanner
 Geometric Numerical Integration: StructurePreserving Algorithms for Ordinary Differential Equations by Hairer, Lubich, and Wanner
Computer Algebra Systems and Computer Arithmetic Edit
 Modern Computer Algebra von zur Gathen and Gerhard
 Modern Computer Arithmetic by Brent and Zimmermann
 Handbook of FloatingPoint Arithmetic by Muller, Brisebarre, de Dinechin, et al.
 Elementary Functions: Algorithms and Implementation by Muller
 Computer Arithmetic: Algorithms and Hardware Designs by Behrooz Parhami
 Synthesis of Arithmetic Circuits: FPGA, ASIC and Embedded Systems by Deschamps, Bioul, and Sutter
 Hardware Implementation of FiniteField Arithmetic by Deschamps, Imana, Sutter
Mathematics Edit
Try to avoid books directly targeting CS majors and/or with titles like "Discrete Math" as they tend to teach next to nothing.
 Introduction to Number Theory by Hardy and Wright
 Combinatorics and Graph Theory by Harris, Hirst, and Mossinghoff
 Combinatorics: Topics, Techniques, Algorithms by Cameron
 All of Statistics: A Concise Course in Statistical Inference by Wasserman
 Probability Models by Sheldon Ross
 Signals and Systems by Oppenheim
 DiscreteTime Signal Processing by Oppenheim
 Analytic Combinatorics by Flajolet and Sedgewick (follow up to their algorithm book)
 Quaternions and Rotation Sequences: A Primer with Applications to Orbits, Aerospace, and Virtual Reality by Kuipers
 Geometry by Brannan, Esplen, and Gray
 Geometric Methods and Applications: For Computer Science and Engineering by Gallier ^{[Website]}
see also Math Textbook Recommendations and Statistics Recommendations
Combinatorial Game Theory Edit
Not to be confused with "Economic" Game Theory, Combinatorial Game Theory studies sequential games where each player has perfect knowledge.
 On Numbers and Games by John H. Conway
 Winning Ways for Your Mathematical Plays: Volume 14 by Elwyn R. Berlekamp, John H. Conway, and Richard K. Guy
 Games, Puzzles, and Computation by Robert A. Hearn and Erik D. Demaine (complexity of games)
 Combinatorial Game Theory (Graduate Studies in Mathematics) by Aaron N. Siegel
 Combinatorial Games: TicTacToe Theory by József Beck
 Games of No Chance, More Games of No Chance, Games of No Chance 3, Games of No Chance 4; Edited by Richard Nowakowski
Quantum Computing Edit
No, quantum computers won't magically be all powerful and able to solve all the world's problems nor is it a sure fire way of solving all the problems in NP but it's still a very interesting and rapidly developing field. You don't need thoroughly study all of the material in Sakurai, Shankar, or Griffiths' Quantum Mechanics texts to read about Quantum Computing but you do obviously need some understanding of QM. The following books will give you all the understanding of what Quantum Mechanics means that you always wanted to know  and if you happen to be a physics student or autodidact, probably never got in Sakurai, Shankar, or Griffiths' books or in the all too common "shut up and calculate!" lectures making them more than worthwhile to study and appreciate even with a QM background as well.
 Quantum Theory: Concepts and Methods by Peres (Covers most of the material you need to know to move into QC)
 Speakable and Unspeakable in Quantum Mechanics: Collected Papers on Quantum Philosophy by Bell
 Quantum Theory by David Bohm (Insight into the relationship between classical mechanics and quantum theory)
If you're interested in learning more about physics and QM see the Physics Textbook Recommendations
 An Introduction to Quantum Computing by Kaye, Laflamme, and Mosca
 Classical and Quantum Computation by Kitaev, Shen and Vyalyi
 Quantum Computation and Quantum Information by Michael Nielsen and Isaac Chuang (The book and reference on QC)
 Quantum Computing: A Short Course From Theory To Experiment by Stolze and Suter (More on the Quantum Engineering side and requires a strong physics background)
Type Theory and Programming Language Theory Edit
Prerequisites: Programming Language Concepts, Proofs. Useful tangential knowledge: Mathematical Logic, Abstract Algebra
 Types and Programming Languages by Pierce ^{[Errata]}
 Advanced Topics in Types and Programming Languages by Pierce ^{[Errata]}
 Practical Foundations for Programming Languages by Harper ^{[Draft]}
 Foundations for Programming Languages by Mitchell
 Formal Semantics of Programming Languages by Winskel
OS Development Edit
 Linux Kernel Development by Love
 Linkers and Loaders by Levine
 Linux Device Drivers by Corbet, Rubini, and KroahHartman
 Understanding the Linux Kernel by Bovet and Cesati
 The Design of the UNIX Operating System by Bach
 The Design and Implementation of the FreeBSD Operating System by McKusick, NevilleNeil, and Watson
 Windows^{®} Internals by Russinovich, Solomon, and Ionescu (For when fate forces you to deal with Windows)
Reverse Engineering and Malware Analysis Edit
Prerequisites: Architecture and Operating Systems. Useful tangential knowledge: Computer Security, OS Development, Unix/Windows, Networks, or Compilers
 Reversing: Secrets of Reverse Engineering by Eilam
 The Shellcoder's Handbook: Discovering and Exploiting Security Holes by Anley, Heasman, Lindner, and Richarte
 Practical Malware Analysis: The HandsOn Guide to Dissecting Malicious Software by Sikorski and Honig
 Practical Reverse Engineering: x86, x64, ARM, Windows Kernel, Reversing Tools, and Obfuscation by Dang, Gazet, and Bachaalany
 The Rootkit Arsenal: Escape and Evasion in the Dark Corners of the System by Blunden
 A Guide to Kernel Exploitation: Attacking the Core by Perla and Oldani
Software Engineering, Development, and Project Management Edit
Prerequisites: Programming. Useful tangential knowledge: Experience with large programs.
 The Mythical ManMonth: Essays on Software Engineering by Brooks
 Code Complete: A Practical Handbook of Software Construction by McConnell
 Design Patterns: Elements of Reusable ObjectOriented Software by Gamma, Helm, Johnson, and Vlissides (Gang of Four book)
 Software Requirements and Specifications: A Lexicon of Practice, Principles and Prejudices by Jackson
 Working Effectively with Legacy Code by Feathers
 The Pragmatic Programmer by Hunt and Thomas
 Clean Code: A Handbook of Agile Software Craftsmanship by Robert C. Martin
 Refactoring: Improving the Design of Existing Code by Fowler
 Peopleware: Productive Projects and Teams by DeMarco and Lister
Databases Edit
 An Introduction to Database Systems by Date
 Database Management Systems by Ramakrishnan and Gehrke
 Readings in Database Systems by Hellerstein and Stonebraker ^{[Red Book Website]}
 Transaction Processing: Concepts and Techniques by Gray and Reuter
 Transactional Information Systems: Theory, Algorithms, and the Practice of Concurrency Control and Recovery by Weikum and Vossen
Distributed Systems and Computing Edit
 Distributed Systems: Principles and Paradigms by Tanenbaum and van Steen
 Distributed Systems by Mullender
 Distributed Algorithms by Lynch
 Introduction to Distributed Algorithms by Tel
 Distributed Computing: Fundamentals, Simulations, and Advanced Topics by Attiya and Welch
Game Development Edit
Prerequisites: Programming & Data Structures, Vector Calculus, Linear Algebra, Intro Physics. Useful knowledge: Algorithms, Architecture, Quaternions, Computer Graphics, AI, Numerical Analysis and Methods, Networks, or Software Engineering.
Yeah, yeah, I can hear you snickering already. These aren't API guides but details on what's under the hood.
Overviews and Engines Edit
 Game Engine Architecture by Gregory (Broad overview of everything that makes a game engine tick)
 Introduction to Game Development by Rabin (Covers development/engines as well as design and production/management/marketing details)
Graphics Edit
See the above sections on general computer graphics and mathematics texts before moving on
 Mathematics for 3D Game Programming and Computer Graphics by Lengyel
 RealTime Rendering by AkenineMoller, Haines, and Hoffman
 Physically Based Rendering: From Theory To Implementation by Pharr and Humphreys
Physics Edit
 RealTime Collision Detection by Ericson
 Game Physics by Eberly
Artificial Intelligence Edit
 Artificial Intelligence for Games by Millington and Funge
 Game AI Pro: Collected Wisdom of Game AI Professionals by Steve Rabin
 Game AI Pro 2 by Steve Rabin (AI algorithms and techniques currently being deployed in recent games)
For more AI theory, see the above section on academic AI texts.
Miscellaneous References Edit
 Hacker's Delight by Warren
 ComputerRelated Risks by Neumann
 Programming Pearls by Bentley
 Structure and Interpretation of Computer Programs by Abelson and Sussman (Known as SICP)
 Association of Computing Machinery (ACM) and IEEE Computer Society's joint undergraduate curricula guidelines and recommendations for Computer Science and Computer Engineering (For comparing what you know with what you should know)