Catalog Description: 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.

Units: 4

Also Offered As: COMPSCI 288

Course Objectives:

• 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.

Prerequisites: CS 288 assumes prior experience in machine learning and strong programming proficiency in PyTorch. Previous coursework in linguistics or natural language processing (e.g., EECS 183/283A, an undergraduate-level NLP course) is recommended but not required.

Grading Basis: Default Letter Grade; P/NP Option

Final Exam Status: No

---

Links:

• EECS Course Archives (CalNet authentication required)
• CS enrollment policies
• EE enrollment policies
• HKN course maps