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The goal E University

Responsible AI in practice

Model cards, copyright in training data and the ethics of generative images — what has to be in place before an AI system meets real users.

Knowledge nodes
54
From zero
about 41 h
Labs
6
See what you already know — no account

The diagnostic removes what you already know, so your path is usually much shorter.

What you can do afterwards

Labs along the way

You write the code. Tests you cannot see decide whether it holds up.

Lab: a neural network in pure NumPy — forward, backprop, gradient checkDa sandbox · about 75 minLab: dot product, norm and cosine similarityDin the browser · about 40 minLab: gradient descent from scratchDin the browser · about 50 minLab: matrix multiplication and one layer of a neural networkDin the browser · about 45 minLab: scaled dot-product attention with a causal maskDa sandbox · about 60 minLab: semantic search with embeddingsDa sandbox · about 45 min

The whole path

Everything the goal builds on, grouped by level and in the order it builds on itself. Show on the map

AExplorer2 knowledge nodes
  1. Patterns and categories
  2. Sequences and precise instructions
BInvestigator7 knowledge nodes
  1. Data in everyday life
  2. Algorithmic thinking
  3. Rule-based systems and machine learning
  4. Source criticism and responsibility in AI use
  5. Safe AI use at work
  6. Training data, features and labels
  7. Classification: how a model sorts information
CBuilder10 knowledge nodes
  1. Functions and coordinate systems
  2. Programming logic — variables, conditions, loops
  3. Python — the basics
  4. Python — lists, loops and dictionaries
  5. Statistics — mean, median and spread
  6. Probability — the basics
  7. Linear regression: fitting a straight line to data
  8. Neural networks — the intuition
  9. Prompting — steering a language model
  10. Responsible use of AI
DAI developer17 knowledge nodes
  1. Derivatives and optimisation
  2. Licences and open data
  3. Loss functions
  4. Tokenisation
  5. Gradient descent
  6. Vectors
  7. Matrices and matrix multiplication
  8. Linear regression with several features
  9. Neural networks — the forward pass with matrices
  10. Backpropagation
  11. Embeddings — words as vectors
  12. Attention
  13. Overfitting and generalisation
  14. PyTorch — tensors and autograd
  15. Training, validation and test
  16. Train a neural network in PyTorch
  17. Transformers — the architecture
EUniversity17 knowledge nodes
  1. Personal data and anonymisation
  2. The GDPR and AI
  3. The EU AI Act
  4. Responsibility and the human in the loop
  5. Children as users of AI
  6. Annotation and labelling of data
  7. Dataset documentation and datasheets
  8. Model cards and system documentation
  9. Copyright and training data
  10. Generative models — an overview
  11. Generative images: copyright, deepfakes, watermarking
  12. Language models — training and generation
  13. Hallucinations — causes and countermeasures
  14. UX for AI features
  15. Model evaluation
  16. Fine-tuning language models
  17. Accessibility and inclusion in AI products
GFrontier Lab1 knowledge nodes
  1. Model extraction and membership attacks