Notes

Mathematical foundations for machine learning — linear algebra, calculus, probability, statistics, optimization, information theory, discrete mathematics, and numerical computing.

The Python stack for machine learning — NumPy, pandas, scikit-learn, PyTorch, TensorFlow, the boosting libraries, visualization, Hugging Face, and the MLOps tooling around them.

Core machine learning — framing a problem, the algorithm families derived from first principles, generalization theory, evaluation, feature engineering, tuning, and the practical and ethical failure modes.

Neural networks from the perceptron up — backpropagation, activations and initialization, optimization, normalization, CNNs, RNNs, transformers, transfer learning, generative models, self-supervised learning, and deep RL.

Natural language processing from tokenization to LLMs — representation, language models, classification and tagging, translation, generation and decoding, RAG, speech, and evaluation.