Johns Hopkins University 2 credits Finance
This course introduces the fundamentals of machine learning and its applications in the finance area. We shall first discuss the difference between traditional econometric models with machine learning. You will then be given an overview of state-of-the-art machine learning topics with applications in finance. The course content is designed to have a balanced combination of theories, computations, and applications. The finance industry is a tremendous consumer of advanced computational and econometric techniques; machine learning is the newest addition to that list. The aim of this course is not only to teach you the underlying machine learning theory, but also train you to apply to the theory in appropriate places in finance. To fulfill that objective, a big component of the course will be based on applied computational projects, where we shall apply the theory learned in this course to real life data. We shall use open-source Python software to learn the implementation of the techniques. The state-of-the-art techniques in computational finance and econometrics is a pre-requisite for a wide spectrum of jobs in the finance industry and this course will help you towards that objective. Prerequisite(s): BU.232.610 AND BU.232.620
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