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IEMS 395-490: Special Topics in IE: Applied Statistical Learning and Decision Making


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Prerequisites

IEMS 304 or CS 349 or equivalent

Description

Description

  • Will explore common problems related to finance and healthcare and how to tackle them using statistical tools. Common techniques we will explore include poisson-regression, binning, bagging (credit rating), time-series analysis methods like unit-root test (stock market), Tweedie regression, and survival analysis (healthcare). 
  • Bi-weekly homework, midterm, final exam, class project. Lab attendance mandatory.
  • This special topics course can be used as an IE/OR elective for Industrial Engineering.

LEARNING OBJECTIVES

  • Familiarizing students with some of the recurring statistical questions in finance and healthcare industry.
  • Designing, conducting, and predicting loan recovery, patient mortality etc. from messy incomplete datasets that are prevalent in the real world.
  • Making informed decisions with confidence. Statistics will not indicate whether a loan should be given. The practitioner will have to decide based on factors.
  • Emphasizing Ex-post over Ex-ante. Providing depth of understanding about a dataset and then choosing a statistical tool to tackle that problem, rather than applying a method on a dataset and improving it based on the outcome.

 TOPICS

There are two broad topics of focus:

  1. Making important decisions about financial risk management

    a. Credit scoring: Score creditworthiness
    b. Risk Modelling: Days-past-due and loan recovery predictions
    c. Trend analysis
    d. (optional) Fraud detection: Predicting willful defaults

  2. Making important decisions about healthcare

    a. Dose escalation: Finding Toxicity probability of a dose.
    b. Survival Analysis: Estimating the time until a specific event (e.g., death, relapse)
    c. (optional) QTL mapping.

  3. Project (tentative): The students will follow the trails of what led to the 2008 financial crisis, and how the ACA helped dampen its effects in healthcare.

 

 MATERIALS

No-required textbook. Optional reading will be presented in class. Computational software: R/Rstudio