CS & AI Tutorials

A growing, in-depth collection of tutorials on computer science, data science, and AI/ML concepts — each one combining theory, visuals, implementation, and real-world use cases in a single place.

The goal: for any concept, you should be able to find one tutorial that takes you from "what is this and why does it matter" through the math, a working implementation, and a look at how it's actually used in practice.

How tutorials are organized

Tutorials live under topic domains at the repo root. Each tutorial is its own folder containing a self-contained notebook and/or report:

<domain>/<tutorial-name>/
├── README.md            # summary, prerequisites, learning objectives
├── tutorial.ipynb        # theory + visuals + implementation, or
├── report.md              # long-form writeup for math-heavy topics, and/or
└── src/                    # supporting code, if the notebook outgrows inline cells

See _template/ for the scaffold used to start a new tutorial, and CONTRIBUTING.md for the full writing guide.

Domains

Domain	Description
math-and-statistics	Linear algebra, probability, calculus, statistical inference
data-structures-algorithms	Core CS: trees, graphs, sorting, complexity analysis
software-engineering	Design patterns, testing, architecture, code quality
systems	Operating systems, networking, distributed systems, concurrency
databases	Relational/NoSQL design, indexing, query optimization, transactions
big-data	Distributed processing, Spark, streaming, data lakes
data-engineering	Pipelines, ETL/ELT, orchestration, data modeling
devops	CI/CD, containers, infrastructure as code, observability
machine-learning	Classical ML: regression, trees, SVMs, clustering, gradient descent
deep-learning	Neural nets, CNNs, RNNs, transformers, attention
generative-ai	LLMs, diffusion models, RAG, agents, prompting
nlp	Text processing, embeddings, language modeling
time-series	Forecasting, seasonality, state-space models
causal-inference	Experiment design, causal graphs, treatment effect estimation
responsible-data-science	Fairness, privacy, interpretability, ethics

Running the notebooks

git clone https://github.com/thompgt/cs-ai-tutorials.git
cd cs-ai-tutorials
conda env create -f environment.yml
conda activate cs-ai-tutorials
jupyter lab

Contributing

New tutorials, fixes, and topic requests are welcome — see CONTRIBUTING.md to get started.

License

Code is licensed under MIT. Written content (reports, notebook markdown, images) is licensed under CC BY 4.0.