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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

Students will explore common problems related to finance and healthcare and how to tackle them using statistical tools. Common techniques discussed include poisson-regression, binning, bagging (credit rating), time-series analysis methods like unit-root test (stock market), Tweedie regression, and survival analysis (healthcare). Class will include 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