Tracks

AI from zero

What AI is, how it learns from examples, and how a chatbot picks its next word.

Start with What is AI, really?

How machines learn

Loss, gradients, and generalization: the loop behind every trained model.

Start with The learning loop

Neural networks

Neurons, layers, and backpropagation, built up one piece at a time.

Builds on How machines learn

Start with Layers are matrix multiplications

Language as vectors

Tokens, embeddings, and similarity: how text becomes geometry.

Builds on How machines learn

Start with Tokens

Transformers and LLMs

Attention, the transformer block, and how large language models are trained.

Builds on Neural networks and Language as vectors

Start with Predicting the next token

Learning by trial and error

Rewards, exploration, and how agents learn to act, from bandits to RLHF.

Builds on How machines learn and Neural networks

Start with Explore or exploit: bandits

Inside the model

Look inside a real language model, watch circuits form, and steer what it says.

Builds on Transformers and LLMs

Start with Reading a model layer by layer

Models that see

How models turn pixels into labels, boxes, masks, depth, matches with words, and answers to questions.

Builds on Transformers and LLMs

Start with How computers see

Entry point: an intermediate lesson that needs only Convolutions (Neural networks) and Attention, step by step (Transformers and LLMs).

Models that hear and speak

How models turn sound waves into spectrograms, labels, and text, and text back into speech.

Builds on Transformers and LLMs

Start with How computers hear

Entry point: an intermediate lesson that needs only Turning things into numbers (AI from zero).

Frontiers

Diffusion, multimodal models, mixture of experts, agents, reasoning, and evaluation.

Builds on Transformers and LLMs

Start with Diffusion: from noise to data

Securing AI

How AI systems are attacked, what defenses catch, and what they miss.

Builds on Frontiers

Start with Attacks on AI systems