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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 RISK MANAGEMENT Machine Learning for Ris | |
| Call Number | 15157 |
| Day & Time Location |
R 8:10pm-10:00pm 302 Hamilton Hall |
| Points | 3 |
| Grading Mode | Standard |
| Approvals Required | None |
| Instructor | David J Romoff |
| Type | LECTURE |
| 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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