IS 6460 Modeling Natural Language 3.0 Credits
Provides a rigorous introduction to the foundations and modern methods of natural language processing (NLP). The course integrates linguistic, statistical, and machine-learning perspectives, beginning with core topics such as text representations, n-gram models, and supervised learning for language tasks. Students then explore neural models and attention distributions produced by transformer-based large language models. Emphasis is placed on the theoretical principles that govern these models, alongside practical considerations in training, fine-tuning, evaluating, and deploying them in real-world applications. Through lectures, programming assignments, and leadership of a team project, students will build, analyze, and responsibly apply NLP methods.
Repeat Status: Not repeatable for credit
Prerequisites: IS 5330 [Min Grade: D] (Can be taken Concurrently) or CS 5520 [Min Grade: D] or DSCI 631 [Min Grade: D] or CS 613 [Min Grade: D]