Natural Language Processing
“principles, techniques, and tools used for analyzing and processing natural language data”
The description names no topic.
- Self-attention and Transformers
- Pretraining (Scaling, Systems, Data)
- Post-training (RLHF, SFT, DPO)
- Agents, Tool Use, and RAG
- Large language model benchmarking and evaluation
- Fine-tune Large Language Models
- Build Reasoning Models
- Transformers and attention
- Pretraining and post-training of language models
- Agents, tool use and retrieval
- Evaluating language models
- Hands-on final project on a real NLP problem
Ask of the departmentDepartment to confirm whether the syllabus already teaches transformers and language models. If not, rebuild the course around them.