Directory of Classes

NOTE: Course information changes frequently, including Methods of Instruction. Please revisit these pages periodically for the most recent and up-to-date course information.

Spring 2022 Enterprise Risk Management PS5555 section 001
Machine Learning for Ris

Call Number 15157
Day & Time
R 8:10pm-10:00pm
302 Hamilton Hall
Points 3
Grading Mode Standard
Approvals Required None
Instructor David J Romoff
Method of Instruction In-Person
Course Description The exponentially increasing availability of data and the rapid development of information technology and computing power have inevitably made Machine Learning part of the risk manager’s toolkit. But, what are these tools? This class provides the driving intuitions for machine learning. Students will see how many of the algorithms are extensions of what we already do with our human minds. These algorithms include regularized regression, cluster analysis, naive bayes, apriori algorithm, decision trees, random forests, and boosted ensembles. Through practical and real-life applications of ML to Risk Management, students will learn to identify the best technique to apply to a particular risk management problem, from credit risk measurement, fraud detection, portfolio selection to climate change, and ESG applications.
Web Site Vergil
Department Enterprise Risk Management
Enrollment 9 students (45 max) as of 5:04PM Wednesday, July 6, 2022
Subject Enterprise Risk Management
Number PS5555
Section 001
Division School of Professional Studies
Campus Morningside
Note Hy-Flex. ERM ONLY; SPS 1/18; PREREQ: ERMCK5350 or exam
Section key 20221ERMC5555K001

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SIS update 07/06/22 17:04    web update 07/06/22 19:36