Topics

Linear algebra, calculus, probability and optimization: the foundations behind the models.

Writing clear, fast and correct Python for scientific work.

Data cleaning, statistics, experiment design and honest evaluation.

Classical learning algorithms, derived and implemented from scratch.

Neural networks, backpropagation and modern architectures.

Convolution, image models and visual recognition.

Language models, embeddings, transformers and generative methods.

Practical guides to the tools of the trade: Git, notebooks, environments.

Reading notes and summaries of papers, with sources.