Summer 2024 Quantitative Methods: Social Sciences GR5067 section 001



Call Number 10316
Day & Time
MT 5:30pm-8:40pm
311 Fayerweather
Points 3
Grading Mode Standard
Approvals Required None
Instructor Patrick Houlihan
Method of Instruction In-Person
Course Description

Social scientists need to engage with natural language processing (NLP) approaches that are found in computer science, engineering, AI, tech and in industry. This course will provide an overview of natural language processing as it is applied in a number of domains. The goal is to gain familiarity with a number of critical topics and techniques that use text as data, and then to see how those NLP techniques can be used to produce social science research and insights. This course will be hands-on, with several large-scale exercises. The course will start with an introduction to Python and associated key NLP packages and github. The course will then cover topics like language modeling; part of speech tagging; parsing; information extraction; tokenizing; topic modeling; machine translation; sentiment analysis; summarization; supervised machine learning; and hidden Markov models. Prerequisites are basic probability and statistics, basic linear algebra and calculus. The course will use Python, and so if students have programmed in at least one software language, that will make it easier to keep up with the course.

Web Site Vergil
Subterm 07/01-08/09 (B)
Department Summer Session (SUMM)
Enrollment 9 students (25 max) as of 10:07AM Sunday, July 14, 2024
Subject Quantitative Methods: Social Sciences
Number GR5067
Section 001
Division Summer Session
Section key 20242QMSS5067G001