IDS 575 Statistical Models and Methods for Business Analytics

Edition: Fall 2017

Document version: July 24 2017

Overview

The goal of this class is to cover the foundations of modern statistics and machine learning complementing the data mining focus of IDS 572. In other words, the objective of the class is to bring students up to speed with the requisite background as well as expose them to the key theoretical underpinnings of modern analytics. We will do so through the lens of statistical machine learning.

Logistics

Textbook and Materials

Software

Topics

  1. 09/02 : Supervised Learning
  2. 09/09 : Regression I: Assumptions, Interpretation, LASSO (Least Absolute Shrinkage and Selection Operator), Feature Hashing
  3. 09/16 : Regression II: Generalized Linear Models, MARS, Categorical variables, Interaction Terms
  4. 09/23 : Classification: LDA and Logistic Regression
  5. 09/30 : Model Assessment and Selection: Bias-variance Tradeoff, Cross-validation, AIC, BIC
  6. 10/07 : Model Inference and Averaging: Maximum Likelihood Estimation, Expectation Maximization, Sampling (Markov Chain Monte Carlo)
  7. 10/14 : Time Series Analysis
  8. 10/21 : In-class Exam I
  9. 10/28 : Gradient Boosting
  10. 11/4 : Support Vector Machine and Convex Duality
  11. 11/11 : Unsupervised Learning: Principal Component and Factor Analysis
  12. 11/18 : High-dimensional Problems
  13. 11/25 : No class (Thanksgiving weekend)
  14. 12/02 : In-class Exam II

Grades

Assignments

Exams

Project

Miscellaneous Information

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