Stanford CME 295: Transformers & Large Language Models course by Afshine Amidi and Shervine Amidi. The official course website contains the syllabus, slides, and links to all lecture recordings.

Official course

• Stanford CME 295 – Transformers & Large Language Models

Official YouTube playlist

• Stanford Online – CME 295 Playlist

Course schedule

1. Transformer – Tokenization, embeddings, attention, Transformer architecture
2. Transformer-Based Models & Tricks – RoPE, MQA/GQA, BERT variants
3. Large Language Models – GPT, MoE, prompting, Chain-of-Thought
4. LLM Training – Pretraining, SFT, LoRA, optimization
5. LLM Tuning – RLHF, DPO, preference tuning
6. LLM Reasoning – Reasoning models, GRPO, scaling
7. Agentic LLMs – RAG, tool calling, ReAct, agents
8. LLM Evaluation – LLM-as-a-Judge, benchmarks, biases
9. Current Trends & Recap – Vision Transformers, diffusion-based LLMs, future directions

If you're looking for a research-level understanding of LLMs (beyond prompt engineering), this is one of the best freely available university courses currently available. It pairs particularly well with:

• Hugging Face NLP Course
• DeepLearning.AI – How Transformer LLMs Work
• Google Machine Learning Crash Course – LLMs