Machine Learning for Social Scientists

Semester
Spring 2023
Instructor(s)
Units
4
Section
1
Number
132B
CCN
32660
Times
Tu/Th 12:30-2pm
Location
MOFF102
Course Description

Social scientists and policymakers increasingly use large quantities of data to make decisions and test theories. For example, political campaigns use surveys, marketing data, and previous voting history to optimally target get out the vote drives. Governments deploy predictive algorithms in an attempt to optimize public policy processes and decisions. And political scientists use massive new data sets to measure the extent of partisan polarization in Congress, the sources and consequences of media bias, and the prevalence of discrimination in the workplace. Each of these examples, and many others, make use of statistical and algorithmic tools that distill large quantities of raw data into useful quantities of interest.

 

Subfield: Empirical Theory and Quantitative Methods

Please note that this course is NOT a substitute for PS3.

Prerequisites

Students must have taken PS 3 or Data 8 (or have equivalent coursework).