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

CS Computer Science

• Mathematics for Computer Science Eric Lehman and Tom Leighton 2004
• Probability Theory: The Logic of Science by E. T. Jaynes

Algorithms

• 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

SWA Software Engineering & Architecture

• 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

ML Machine Learning

• 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

• 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