Notes

Personal notes condensed from coursework and reading, in three tracks.

• Reinforcement learning: from MDPs and POMDPs to tabular, approximate and model-based RL, in six chapters.
• LLMs: notes and projects on large language models, starting with a fully local RAG system.
• Statistical ML: classical machine learning in eight parts, from linear regression to neural networks.