Applied Machine Learning for the Social Sciences
GOV 2018
Subject & Catalog Number
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Course Information
Description
This course introduces students to foundational concepts, models, and algorithms in machine learning. We begin with regularization and tools to avoid overfitting in linear and non-linear models. Then, we show how these models apply both to measurement of latent concepts and to estimation of causal effects.
Course Notes
Prerequisites: Gov 2001, Gov 2002, and Gov 2003.
Class Notes
Instructor: Sam Fuller
School
Faculty of Arts & Sciences
Credits
4
Cross Reg
Available for Harvard Cross Registration
Department
Government
Course Component
Seminar
NOTE: This course requires additional sections; you will be prompted to choose secondary components during the Add to Cart process
Subject
Government
Grading Basis
FAS Letter Graded
Course Requirements
Course open to Graduate Students Only
General Education
N/A
Quantitative Reasoning with Data
N/A
Divisional Distribution
Social Sciences
Course Level
Primarily for Graduate Students
