Lectures
Machine Learning: From Data to Large Language Models
An ~80–90 min introduction (no prior ML needed): fundamentals, problem types, production ML systems, embeddings & Transformers, using LLMs (RAG, agents, local models), shipping ML in a real platform, ethics & privacy — ending in a hands-on capstone case study.
Open slides →Generative AI: Reasoning Models & Agents
A follow-on lecture: models that think before answering — chain-of-thought, test-time compute, and how reasoning is trained with reinforcement learning from verifiable rewards (RLVR / GRPO, DeepSeek-R1) — then how reasoning + tools + a loop becomes an agent, and how fast agent capability is growing.
Open slides →