Machine Learning

Professor: Osoba
Units: 0.5
Elective Course
Concentration: Quantitative Methods

Machine learning approaches leverage the ability of statistical methods to recognize patterns and trends in data. This ability holds a lot of promise for tackling questions of public policy especially in data-rich policy areas like manpower, logistics, health, transportation and others.

The goal of this course is to develop a facility with the key methods of the field and an intuitive grasp of their strengths, limitations, and trade-off spaces. This course will discuss key machine learning concepts and approaches. We will briefly discuss the history of the field and the current state-of-art (e.g. deep neural networks, convolutional nets, graphical methods). And we will cover the basic methods (clustering, perceptrons, artificial neural networks, and decision trees) in depth.