Computer science is not about computers, any more than astronomy is about telescopes, or biology about microscopes
-- Attributed to Edsger Dijkstra, Alan Perlis, Jacques Arsac, George Johnson, Donald Knuth, Matthew Dennis Haines
- Mathematics for Computer Science Eric Lehman and Tom Leighton 2004
- Probability Theory: The Logic of Science by E. T. Jaynes
- Introduction to Algorithms by Cormen, Leiserson, Rivest, Stein
- The Algorithm Design Manual by Steven Skiena
- Algorithms in C++ by Sedgewick
- Information Retrieval: Data Structures & Algorithms edited by William B. Frakes and Ricardo Baeza-Yates
- Algorithms by Papadimitriou, Dasgupta
- Algorithms on Strings, Trees, and Sequences by Gusfield
- Randomized Algorithms by Motwani, Raghavan
- Latency Numbers Every Programmer Should Know and evolution over time by Jeff Dean (Google) and Norvig
- What Every Programmer Should Know About Memory
- What Every Computer Scientist Should Know About Floating-Point Arithmetic
- Computer Organization аnd Design by Patterson 5th еdition 2014
- Site Reliability Engineering How Google Runs Production Systems
- Structured Parallel Programming Patterns for Efficient Computation by Michael McCool et al.
- Applied Cryptography by Bruce Schneier
- Designing Data-Intensive Applications by Martin Kleppmann 2016
- Planning Extreme Programming by Fowler and Beck
- Refactoring by Kent Beck, and Martin Fowler
- Computer Architecture by Tanenbaum
- Effective C++ by Scott Meyers
- C++ Concurrency in Action by Anthony Williams
- Clean Code by Robert Martin
- POSA Pattern-oriented Software Architecture by by Frank Buschmann et al.
- Unix Network Programming by Richard Stevens
- Compilers Principles, techniques, and tools by Aho 2007
- Computer systems a programmer’s perspective by Bryant, Hallaron
- Readings in Database Systems, 5th Edition by Bailis et al.
- Security Engineering by Ross Anderson 3rd edition 2020
- An Introduction to Statistical Learning. 2016. ISLR Sixth Printing
- Machine Learning A Probabilistic Perspective by Kevin P. Murphy
- David MacKay. Information Theory, Inference, and Learning Algorithms
- Elements of Causal Inference by Jonas Peters, Dominik Janzing, and Bernhard Scholkopf
- The Elements of Statistical Learning by Trevor Hastie et al.
- Rules of Machine Learning: Best Practices for ML Engineering by Google
- Probabilistic Graphical Models by Daphne Koller, Nir Friedman
- Machine Learning by Tom M. Mitchell
- AI, a Modern Approach by Russel, Norvig (3rd Edition)
- Levels of Organization in General Intelligence by Yudkowsky 2002
- Artificial Intelligence and Games by Yannakakis and Togelius