ORIE 5260

Machine Learning for Finance

Course Description

Course information provided by the Courses of Study 2017-2018.

This course provides a general introduction to machine learning with a view towards applications in finance. The goal is to provide both a solid grounding in the mathematical foundations of machine learning as well as a conceptual map of the field and its relation to areas like statistics and optimization that are currently more familiar in finance. The emphasis is on mathematical understanding, not implementation or financial specifics. Sample topics include generalized linear models, loss functions and regularization, sparsity, support vector machines, kernelization, principal components analysis, clustering, and the EM algorithm. Distinctions between classes of methods, such as probabilistic vs. variational models, Bayesian vs. frequentist approaches, and convex vs. nonconvex models.

When Offered Spring.

Permission Note Enrollment limited to: ORIE MEng Financial Engineering students in New York City.
Prerequisites/Corequisites Prerequisite: Basic knowledge of linear algebra, probability, and optimization at the level of MATH 2940, ORIE 5500, and ORIE 5300. Students should be familiar with, e.g., matrix algebra, eigendecomposition and singular value decomposition, gradients; discrete and continuous distributions, conditional probability and Bayes' rule; and duality in linear programming.

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