Thomas H. Cormen, Charles E. Leiserson, Ronald L. Rivest & Clifford Stein
A comprehensive reference for data structures, algorithm design and complexity analysis. Useful for learning to justify correctness and compare computational costs.
Harold Abelson & Gerald Jay Sussman, with Julie Sussman
Develops computational thinking through abstraction, recursion, interpreters and program structure. The linked comparison edition places Scheme and JavaScript treatments side by side.
Connects programs to the machine beneath them: data representation, assembly, memory, linking and concurrency. A strong foundation for systems and security work.
Remzi H. Arpaci-Dusseau & Andrea C. Arpaci-Dusseau
Explains operating systems through virtualization, concurrency and persistence. Clear examples and exercises help readers understand processes, memory, threads and storage.
Examines reliability, scalability and the tradeoffs behind databases and distributed systems. Helps students reason about replication, consistency and data-processing architectures.
Introduces networking from applications down to transport, routing and the link layer. Companion Wireshark labs connect protocol concepts with real packet traces.
Uses packet-level examples to explain how TCP/IP protocols behave in practice. Particularly useful for traffic analysis, troubleshooting and understanding protocol edge cases.
Provides a broad foundation in threats, access control, software security and defensive mechanisms. A useful bridge between introductory coursework and applied security projects.
Explores how dependable, secure systems are built—and why they fail. Combines technical mechanisms with human factors, economics and lessons from real deployments.
Focuses on using cryptography correctly within real systems, including key management and protocol design. Helps readers recognize the gap between secure primitives and secure implementations.
A broad introduction to intelligent agents, search, reasoning, planning, uncertainty and learning. Provides a conceptual map for choosing an AI research direction.
Builds machine learning from probability, Bayesian inference and statistical models. Useful for understanding model assumptions, uncertainty and the mathematics behind learning algorithms.
Explains neural-network fundamentals, optimization, regularization and representation learning. A mathematical foundation for understanding modern deep models and their limitations.
Presents supervised and unsupervised learning through a probabilistic framework. Connects core theory with modern methods and accompanying computational examples.
Introduces learning through interaction, including Markov decision processes, value functions, temporal-difference learning and policy gradients. Useful preparation for sequential decision-making and agent research.