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 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.
- Identify large scale data analysis methods, focusing on three main problem paradigms: prediction, modeling, and detection.
- Translate policy questions into paradigms.
- Choose and apply the appropriate artificial intelligence and machine learning tools.
- Interpret, evaluate, and apply the results for policy analysis and decision making.
Faculty: Artur Dubrawski
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