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Course Details

Course Number: 90-866

Large Scale Data Analysis for Public Policy

Units: 6

The past decade has seen the increasing availability of very large scale
data sets, arising from the rapid growth of transformative technologies
such as the Internet and cellular telephones, along with the development
of new and powerful computational methods to analyze such datasets. Such
methods, developed in the closely related fields of machine learning, data
mining, and artificial intelligence, provide a powerful set of tools for
intelligent problem-solving and data-driven policy analysis. These
methods have the potential to dramatically improve the public welfare by
guiding policy decisions and interventions, and their incorporation into
intelligent information systems will improve public services in domains
ranging from medicine and public health to law enforcement and security.

This course will provide a basic introduction to large scale data analysis
methods, focusing on three main problem paradigms (prediction, modeling,
and detection). Students will learn how to translate policy questions
into these paradigms, choose and apply the appropriate artificial
intelligence and machine learning tools, and correctly interpret,
evaluate, and apply the results for policy analysis and decision making.
We will emphasize tools that can "scale up" to real-world policy problems
involving reasoning in complex and uncertain environments, discovering new
and useful patterns, and drawing inferences from large amounts of
structured, high-dimensional, and multivariate data. No previous
knowledge of artificial intelligence or machine learning is required.

(Note: this course was previously listed as "Artificial Intelligence Tools for Policy", and is not open to students who have previously taken 90-866.)

Syllabus

Faculty:
Daniel B. Neill