The AI University Map of AI¶

A complete, cross-linked body of knowledge covering every area of artificial intelligence — from foundations to frontier. Free, and growing.

This is an ontology of AI: the whole field organised into connected areas you can explore. Start anywhere and follow the links. Each area deepens over time, and the practitioner topics feed directly into our courses.

Explore the map¶

Foundations of AI ¶

What artificial intelligence is, where it came from, and the ideas every other area builds on.

Machine Learning ¶

Algorithms that improve at a task by learning patterns from data instead of being explicitly programmed.

Deep Learning ¶

Machine learning with many-layered neural networks that learn representations directly from raw data.

NLP & Large Language Models ¶

Getting machines to understand and generate human language — now dominated by large language models.

Computer Vision ¶

Teaching machines to interpret images and video — from recognition to generation.

Generative AI ¶

Models that create new content — text, images, audio, video, and code — rather than only classifying it.

Reinforcement Learning ¶

Learning to act by maximizing cumulative reward through interaction with an environment.

AI Agents & Autonomy ¶

Systems that plan and take actions toward goals — using tools, memory, and (often) other agents.

Robotics & Embodied AI ¶

AI that senses and acts in the physical world through bodies — robots, drones, and vehicles.

Speech & Audio AI ¶

Understanding and generating sound — speech, music, and everything in between.

Knowledge & Reasoning ¶

Representing knowledge explicitly and reasoning over it — the symbolic tradition and its fusion with learning.

Data & MLOps ¶

The engineering that turns models into reliable products — data pipelines, deployment, and monitoring.

AI Safety, Alignment & Ethics ¶

Making AI systems reliable, fair, and aligned with human values — and governing their use.

Applications & Industry ¶

Where AI creates value — a tour of the fields being reshaped by it.

Tools & Ecosystem ¶

The frameworks, platforms, hardware, and benchmarks practitioners actually use.

Building with AI ¶

The practitioner track — how to actually build useful AI products. This is where our courses go deep.

Contribute & follow along

New areas and deep-dives are added continually. This knowledge base is published by AI University.