COMPSCI 288
Advanced Natural Language Processing
This course provides a graduate-level introduction to Natural Language Processing (NLP). We will survey foundational approaches such as word representations and n-gram language models, followed by neural methods including recurrent networks and attention mechanisms, and then progress to modern Transformer-based architectures. In addition, the course will cover advanced topics in contemporary NLP, including retrieval-augmented models, mixture-of-experts architectures, AI agents, and memorization.
Topics
- Learn foundational concepts in NLP, such as word representations, recurrence, attention, and n-gram language models. Learn concepts related to modern language models, including Transformers, pre-training, post-training, fine-tuning, reasoning models, and evaluation. Learn contemporary NLP topics, including retrieval-augmented models, mixture-of-experts, AI agents, and memorization.
Official catalogue course objectives and student learning outcomes
Sources
- Course catalogue ↗2026-2027