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COMP_SCI 461: Deep Learning for Natural Language Processing

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Prerequisites

COMP_SCI 212 and COMP_SCI 336 (or similar courses) or CS MS or CS PhDs

Description

In the first half of this course, we will explore the evolution of deep neural network language models, starting with n-gram models and proceeding through feed-forward neural networks, recurrent neural networks and transformer-based models.  In the second half of the course we will apply these models to natural language processing tasks, including question answering, text classification (including fakes detection), text summarization, text generation (including dialogue, neural machine translation and program synthesis) and natural language inference, among others.  After completing this course, students will be able to generalize these techniques to a wide variety of applied and research problems in natural language processing.

• Formerly Comp_Sci 497 - last offer was Winter 2023
• This course fulfills Project or Technical Elective area.

COURSE COORDINATORS: Prof. David Demeter

COURSE INSTRUCTOR: Prof. David Demeter