The AI Map — for curious beginners

The big ideas in artificial intelligence, explained in plain language. No maths, no jargon — just the ideas, one at a time.

This is the beginner edition of the AI University Map of AI. Same map of the field, written to be easy to follow. Pick anything that looks interesting and start reading.

The whole field at a glance

flowchart TB
  AI([Artificial Intelligence]):::root
  subgraph Learn [Learning paradigms]
    ML[Machine Learning]
    DL[Deep Learning]
    RL[Reinforcement Learning]
    CAU[Causal Inference]
  end
  subgraph Mod [Modalities & generation]
    NLP[NLP & LLMs]
    CV[Computer Vision]
    SP[Speech & Audio]
    GEN[Generative AI]
    MM[Multimodal AI]
  end
  subgraph Sys [Systems & reasoning]
    AG[Agents & Autonomy]
    RO[Robotics]
    KR[Knowledge & Reasoning]
    IR[Retrieval & Search]
    REC[Recommenders]
    TS[Time Series]
  end
  subgraph Infra [Compute & practice]
    DO[Data & MLOps]
    HW[Hardware & Compute]
    EDGE[Edge AI]
    EVAL[Evaluation]
  end
  subgraph Resp [Responsible AI]
    SAF[Safety & Alignment]
    ETH[Ethics & Governance]
    INT[Interpretability]
    PRIV[Privacy & Security]
  end
  AI --> FOUND[Foundations]
  AI --> Learn
  AI --> Mod
  AI --> Sys
  AI --> Infra
  AI --> Resp
  AI --> SCI[AI for Science]
  AI --> APP[Applications]
  AI --> TOOL[Tools & Ecosystem]
  AI --> BUILD[Building with AI]
  classDef root fill:#4f46e5,color:#fff,stroke:#3730a3,stroke-width:2px;

Explore every area

• Foundations of AI

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

• Machine Learning

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

• Deep Learning

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

• NLP & Large Language Models

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

• Computer Vision

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

• Generative AI

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

• Reinforcement Learning

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

• AI Agents & Autonomy

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

• Robotics & Embodied AI

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

• Speech & Audio AI

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

• Knowledge & Reasoning

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

• Data & MLOps

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

• AI Safety, Alignment & Ethics

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

• Applications & Industry

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

• Tools & Ecosystem

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

• Building with AI

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

• Multimodal AI

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  Models that perceive and reason across more than one kind of data at once — text, images, audio, and video together.

• Recommender Systems

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  The AI behind personalization — deciding what to show, suggest, or rank for each user. The quiet workhorse of the internet.

• AI Hardware & Compute

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  The silicon and systems that make modern AI possible — and the single biggest practical constraint on what gets built.

• Evaluation & Benchmarks

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  How we measure whether AI actually works — the science, and real difficulty, of knowing if a model is any good.

• Interpretability & Explainability

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  Opening the black box — understanding why a model made a prediction, and what it has actually learned inside.

• Privacy & Security in AI

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  Protecting data and defending models — training on sensitive data safely, and keeping AI systems robust against attack.

• AI Ethics & Governance

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  The societal side of AI — fairness, accountability, and the laws and norms now shaping how AI can be built and used.

• AI for Science

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  AI as a scientific instrument — accelerating discovery in biology, chemistry, physics, mathematics, and beyond.

• Causal Inference

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  Moving beyond correlation to cause — the tools for asking 'what if?' and 'why?', not just 'what is likely?'.

• Time Series & Forecasting

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  Learning from data that unfolds over time — predicting the future and spotting the unusual.

• Information Retrieval & Search

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  Finding the right information in huge collections — the foundation of search engines and of retrieval-augmented generation.

• Edge & On-Device AI

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  Running AI where the data is — on phones, sensors, and microcontrollers — without a round trip to the cloud.

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Want more depth?

When you're ready for the full detail, the complete Map of AI has every area in depth.