Linguistics: Intro to Computational Ling

See Schedule of Courses for specific titles.

Open to degree and PACE students Special Topics courses cannot carry CC designations.

This course provides an introduction to Python programming and the fundamentals of natural language processing. This course gives a brief introduction to a broad range of important topics in natural language processing, including regular expressions, dependency parsing, embedding models and language models, and introduces fundamental concepts in machine learning and neural networks.

By the end of this course, students should have a broad awareness of the main areas and applications of natural language processing, including string processing with regular expressions, syntactic dependency parsing, and semantics with embedding models. They should develop a basic proficiency with the Python programming language, and be familiar with variables, objects, flow control, functions, and file reading. It is not expected that students will be able to develop complex NLP technologies from scratch, but students should have enough knowledge to deploy existing NLP libraries to develop applications for their own tasks. Finally, students should understand the fundamentals of machine learning, including an awareness of basic machine learning algorithms, preparing datasets for machine learning, and machine learning evaluation. After taking this course, students should be prepared for an advanced NLP course, or an internship in natural language processing.

Weekly homeworks Course Project The weekly homeworks will be short programming assignments designed to give students hands-on experience with the content covered in class. As this is a beginner course, these assignments will encourage students to use existing programming libraries and projects, and focus more on applications rather than the underlying mechanics of NLP technologies. The course project will be a larger programming project in which students apply the natural language processing skills that they have learned throughout the course. They will first propose a project idea to the instructor before Week 9, to which the instructor will give feedback and guidance for their final submission. The program itself, and a report describing it, will be due at the end of the semester.