Computer Science

Notes on computer science concepts, organized from foundational theory to systems, machine learning, and applied subjects.

An ENIAC installation shows early computer architecture as cabinets, switches, and wiring.

Figure: ENIAC gives architecture pages a physical reference point before modern chips. Image: Wikimedia Commons, Paul W. Shaffer and TexasDex, CC BY-SA 3.0/GFDL.

An artificial neural network diagram shows input, hidden, and output layers connected by weights.

Figure: Layered neural networks make differentiable function approximation visible. Image: Wikimedia Commons, Cburnett, CC BY-SA 3.0/GFDL.

A Waymo self-driving car drives on a public road with roof-mounted sensors.

Figure: A real autonomous vehicle grounds the driving stack in a physical platform. Image: Wikimedia Commons, Grendelkhan, CC BY-SA 4.0.

Figure: Git is a practical substrate for collaboration, branching, review, and release workflows. Image: Wikimedia Commons, Jason Long, CC BY 3.0.

Topics

Foundations

Programming Languages — C, C++, Rust, Java, Python, R
Algorithms — sorting, searching, DP, graph algorithms
Data Structures — arrays, trees, hash tables, graphs
Theory of Computation — automata, complexity, computability

Systems

Operating Systems — processes, scheduling, memory, files
Databases — relational model, transactions, indexing
Computer Architecture — ISA, pipelines, caches, parallelism
Embedded & Microprocessors — 8085, 8051, microcontrollers, peripherals
Control Engineering — Laplace, root locus, Bode/Nyquist, state-space, digital control

Machine Learning

Data Mining — clustering, classification, association rules
Machine Learning — classical statistical learning, decision trees, Bayesian methods
Deep Learning — CNNs, RNNs, Transformers, modern architectures
Reinforcement Learning — MDPs, Q-learning, policy gradients, deep RL
Adversarial Attacks — robustness, white/black-box attacks, defenses, certified bounds

Applied

Natural Language Processing — language models, parsing, semantics
Cryptography — symmetric/public-key, security proofs
Software Engineering — design, testing, process
Autonomous Driving — perception, prediction, planning, control, safety

Languages used in code samples

Python — algorithmic clarity, ML/DL/NLP, data mining
C / C++ — systems, data structures, embedded
Rust — safe systems programming
Java — OOP, concurrency
R — statistics and visualization
MATLAB — control engineering, signal processing

Topics​

Foundations​

Systems​

Machine Learning​

Applied​

Languages used in code samples​