The Default Library
Majors / Computer Science / Specialization

AI Development

Building with artificial intelligence: how neural networks and large language models work, how to turn them into products that hold up, and the safety and governance questions that come with them.

A specialization of Computer Science. It assumes the first two years of the major and gives the last two a focus.

For: computer science students heading into AI, developers who want to build with models rather than just call them, and anyone who wants to know what is inside the systems now writing and drawing.

Shaped by Stanford's CS224N (Natural Language Processing with Deep Learning) and CS229, the fast.ai and Hugging Face courses, the ACM/IEEE CS2023 AI knowledge area and the international AI safety reports.

6readings
1.4 hoursof reading
2stages
4 reads · 2 deep dives
Not started
Start the path
Stage 1

Year 3 · How the models work

3 readings · 38 min
  1. 01ReadInteractiveQuiz11 min

    Machine Learning: Programs That Write Their Own Rules

    How machines learn patterns from data.
  2. 02ReadInteractiveQuiz14 min

    Deep Learning: How Stacked Simple Layers Learn to See

    Neural networks, backpropagation and why depth works.
  3. 03ReadInteractiveQuiz13 min

    Large Language Models & Generative AI: Predicting the Next Piece, at Planetary Scale

    Transformers, attention, pretraining and how text and images are generated.
Stage 2

Year 4 · Building and governing

3 readings · 46 min
  1. 04Deep diveInteractiveQuiz17 min

    Building AI Applications: The Model Is a Part, Not a Product

    Prompts, retrieval, agents, evaluation and putting a model in a product.
  2. 05Deep diveInteractiveQuiz15 min

    Data Ethics, Privacy & Fairness: Who a Model Can Harm

    Privacy, bias and fairness in data-driven systems.
  3. 06ReadInteractiveQuiz14 min

    AI Safety, Alignment & Governance: What We Wrote Versus What We Meant

    Alignment, misuse, evaluation and the laws now being written for AI.

Study it in depth, for free

These pieces give you the intuition. To learn the major properly, these are the courses and texts universities use, free to read or audit, and the standard textbooks if you want to own one. Links checked November 2026.

The whole major, free: Stanford CS224N (deep learning for language, through transformers) with the Hugging Face LLM Course for building, followed by the International AI Safety Report and Fairness and Machine Learning for Year 4 governance.

Free courses and texts

  1. Introduction to Machine Learning · MIT OCW 6.036 · supervised learning, regression, classification and neural nets; Year 3 learning from data
  2. CS229: Machine Learning · Stanford · the rigorous mathematical treatment of machine learning, with notes; Year 3
  3. Introduction to Deep Learning · MIT OCW 6.S191 · neural networks, backpropagation, CNNs and generative models in a short course; Year 3
  4. Dive into Deep Learning · Zhang, Lipton, Li and Smola, free interactive book with code · neural networks to transformers; Year 3
  5. Neural Networks: Zero to Hero · Andrej Karpathy · building backpropagation and a GPT from scratch in code; Year 3
  6. Stanford CS224N: NLP with Deep Learning · Stanford · attention, transformers, pretraining and large language models; Year 3
  7. Practical Deep Learning for Coders · fast.ai · training and deploying models top-down; Years 3-4
  8. Hugging Face LLM Course · Hugging Face · using, fine-tuning and sharing transformer models; Year 4 building with models
  9. Hugging Face AI Agents Course · Hugging Face · tools, retrieval and agents; Year 4
  10. Fairness and Machine Learning: Limitations and Opportunities · Barocas, Hardt and Narayanan, free online book · bias, fairness and measurement; Year 4 privacy and fairness
  11. International AI Safety Report · international expert-panel report · risks, misuse and evaluation of advanced AI; Year 4
  12. EU Artificial Intelligence Act · Future of Life Institute resource tracking the law's text and analysis (an independent site, not the EU itself); Year 4 regulation

The standard textbooks

  • Artificial Intelligence: A Modern Approach (Stuart Russell and Peter Norvig) · the standard AI survey text; Years 3-4
  • Deep Learning (Ian Goodfellow, Yoshua Bengio and Aaron Courville) · the standard theory text, free online at deeplearningbook.org; Year 3
  • Speech and Language Processing (Daniel Jurafsky and James H. Martin) · NLP and language models, with the draft third edition free online at web.stanford.edu/~jurafsky/slp3/; Years 3-4
  • Probabilistic Machine Learning: An Introduction (Kevin P. Murphy) · rigorous machine-learning foundations, free draft at probml.github.io/pml-book/; Year 3
  • An Introduction to Statistical Learning (Gareth James, Daniela Witten, Trevor Hastie and Robert Tibshirani) · free at statlearning.com; for self-learners; Year 3
  • The Alignment Problem (Brian Christian) · accessible account of bias, fairness and safety; Year 4
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