CS 480P - Natural Language Processing

Credits: 3

In this course, students will gain a thorough introduction to understanding natural language processing (NLP) tasks and appropriate models/tools to solve them. Recent years have witnessed the growth of statistical NLP and machine learning models for several NLP tasks such as translation, summarization, parsing, part-of-speech tagging, etc. This course will introduce students to machine-learning models for different tasks in the NLP domain. Particularly, in recent years, Deep Learning approaches have obtained very high performance and are the state-of-the-art across many different NLP tasks. The course will also provide an overview of deep learning approaches and how to effectively use them in the context of NLP tasks. Through lectures, assignments, exams, and a final project, students will learn the necessary skills to understand different NLP tasks and design, and implement solutions. Prerequisite (required): CS 350, CS 375, MATH 327. Offered once a year.