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Machine learning and public policy

Machine

 

edward mcfowland  Profesor Edward McFowland, Universidad de Minnesota

Alvaro Riascos  Profesor Alvaro Riascos, Universidad de los Andes

Junio 25 a Julio 7 2018
Clase el sábado 7 de julio. 

Álvaro Riascos: 25-29 junio 2:00 p.m. a 5:00 p.m.
Monitoria: 5:00 p.m. a 6:00 p.m.                

Edward McFowland: 3-6 julio 2:00 p.m. a 5:00 p.m.
Monitoria: 5:00  p.m. a 6:00 p.m.                
Edward McFowland: 7 julio (sábado): 8:00 a.m. a 11:00 a.m.

Monitoria: 11:00 a.m. a 12:00 p.m.

Curso en inglés y español

The age of Big Data has catalyzed the maturation and widespread adoption of machine learning. It is now more critical than before that practitioners across the social science disciplines become  familiar with machine learning methods, understanding both the potential and limits of these methods in the social sciences. Therefore, the first objective of this course is to introduce participants to modern machine learning methods. This will provide an understanding of the fundamental building blocks upon which these methods are built; enabling participants to be insightful practitioners of modern machine learning. The second objective of this course is to introduce some of the recent and advanced developments of machine learning with a specific focus on public policy. This will provide an understanding of the role of machine learning in improving public policy, and address challenges across the social science problem spectrum. Participants should leave this course empowered to think critically about the intersection of machine learning and social science, and develop machine learning solutions that inform and solve real-world policy problems.

Prerrequisitos: Microeconomía III, Macroeconomía III y Econometría I.

Programa preliminar del curso

Guía de inscripción y calendario académico 
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