COMPSCI288
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COMPSCI 288 - Advanced Natural Language Processing
Subject
COMPSCI
Course Number
288
Department
Course Level
Graduate
Course Title
Advanced Natural Language Processing
Course 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.
Minimum Units
4
Maximum Units
4
Grading Basis
Default Letter Grade; S/U Option
Instructors
Klein, Min, Suhr
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.
Repeat Rules
Course is not repeatable for credit.
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.
Formats
Lecture
Term
Fall and Spring
Weeks
15 weeks
Weeks
15
Lecture Hours
3
Lecture Hours Min
3
Lecture Hours Max
3
Lecture Mode of Instruction
In Person
Outside Work Hours
9
Outside Work Hours Min
9
Outside Work Hours Max
9