Machine learning techniques are now very common in many spheres, and there is a growing popularity of these approaches in macroeconomic forecasting as well. Are these techniques really useful in the prediction of macroeconomic outcomes? Are they superior in performance compared to their traditional counterparts? We carry out a meta-analysis of the existing literature in order
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Instructor:
Teaching Fellow:
Gal Koplewitz, galkop@gmail.com. Office hours: Thursdays 3-4, Maxwell Dworkin second floor lounge.
This course examines papers every computer scientist should have read, from the 1930s to the present. It is meant to be a synthesizing experience for advanced students in computer science: a way for them to see the field as a whole, not through a survey, but by reliving the experience of its creation. The idea is to create a unified view of the field of computer science, for students who already know something about it, by replaying its entire evolution at an accelerated frame rate.