IDS 575 Statistical Models and Methods for Business Analytics

Edition: Spring 2018

Document version: Apr 24 2018

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

Timeline

Lectures (tentative)

  1. 01/20 : Supervised Learning: Linear Models and Least Squares, k-Nearest Neighbor Methods
  2. 01/27 : Towards Regression: Statistical Decision Theory, Curse of Dimensionality, Linear Regression, Categorical Variables, Interaction Terms
  3. 02/03 : Regression I: Bias-variance Trade-off, Subset Selection, Cross-Validation
  4. 02/10 : Regression II: Ridge Regression, LASSO (Least Absolute Shrinkage and Selection Operator)
  5. 02/17 : Classification: Linear Discriminant Analysis, Logistic Regression, Model Assessment and Selection: AIC, BIC and Validation
  6. 02/24 : The Bootstrap and Maximum Likelihood Estimation
  7. 03/10 : Expectation Maximization and Sampling (Markov Chain Monte Carlo) (video)
  8. 03/17 : Tree Methods, Adaboost and Gradient Boosting
  9. 03/24 : Random Forests, Multivariate Adaptive Regression Splines and Support Vector Machines (video)
  10. 04/07 : Kernel Trick, Introduction to Unsupervised Learning, Association Rules (video)
  11. 04/14 : Unsupervised Learning: Clustering, Principal Component Analysis and Spectral Clustering (video)
  12. 04/21 : Time Series and Supervised Learning, The ARMA Model
  13. 04/28 : Project Presentations

(A concatenated set of scribed notes are available here)

Assignments

  1. 01/27: Assignment 1 out. Due on 02/12 0759hrs
  2. 02/10: Assignment 2 out. Due on 02/26 0759hrs
  3. 03/24: Assignment 3 out. Due on 04/23 0759hrs

Exams

  1. 03/03: Exam I (same venue as lectures, and during class hours)
  2. 05/05: Exam II (same venue as lectures, and during class hours)

Project

  1. 03/19 0759hrs: Project Report I due
  2. 04/30 0759hrs: Project Report II due

Grades

Assignments

Exams

Project

Miscellaneous Information

Back to the instructor's homepage.