Statistical Models and Methods for Business Analytics
IDS 575 (Spring 2019)
Document version: Jan 27 2019
All announcements will be made on the Forum
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, you will get up to speed with the requisite background as well as the key theoretical underpinnings of modern analytics. We will do so through the lens of statistical machine learning.
- Lectures: Mondays 6.00 PM to 8.30 PM at BH 208
- Optional Recitations: Thursdays 11 AM to 12 noon at BH 309
- Online communication: Forum (sign up needed!)
- Offline communication:
- Instructor Office Hours: Wednesdays 3.00 PM to 4.30 PM at UH 2404
- TA Office Hours: Tuesdays 5 PM to 7 PM at UH2432
Textbook and Materials
01/14 : Supervised Learning: Linear Models and Least Squares, k-Nearest Neighbor Methods
01/28 : Towards Regression: Statistical Decision Theory, Curse of Dimensionality, Linear Regression, Categorical Variables, Interaction Terms
02/04 : Regression I: Bias-variance Trade-off, Subset Selection, Cross-Validation
02/11 : Regression II: Ridge Regression, LASSO (Least Absolute Shrinkage and Selection Operator)
02/18 : Classification: Linear Discriminant Analysis, Logistic Regression, Model Assessment and Selection: AIC, BIC and Validation
02/25 : The Bootstrap, Maximum Likelihood Estimation and Review of Linear Models
03/11 : Expectation Maximization and Sampling (Markov Chain Monte Carlo)
03/18 : Applications of regression, classification and likelihood maximization
04/01 : Tree Methods, Adaboost and Gradient Boosting
04/08 : Random Forests, Multivariate Adaptive Regression Splines and Support Vector Machines
04/15 : Kernel Trick, Introduction to Unsupervised Learning, Association Rules
04/22 : Unsupervised Learning: Clustering, Principal Component Analysis and Spectral Clustering
04/29 : Time Series and Supervised Learning, and the ARMA Model
- 01/28: Assignment 1 out. Due on 02/10
- 02/11: Assignment 2 out. Due on 02/24
- 02/25: Assignment 3 out. Due on 03/17
- 04/01: Assignment 4 out. Due on 04/14
- 04/15: Assignment 5 out. Due on 04/28
These involve reimplementing statistical techniques and understanding their behavior on interesting datasets. Always mention sources in your assignment solutions. Submission deadline is BEFORE 11.59 PM on the concerned day. Late submissions will have an automatic 20% penalty per day. Use Blackboard for uploads.
- 03/04: Exam I (same venue as lectures, and during class hours)
- 05/06: Exam II (same venue as lectures, and during class hours)
These are closed book, but one 8.5x11-inch handwritten cheatsheet is allowed. No computers and communication devices are allowed.
- Assignments: 8% + 8% + 8% + 8% + 8%
- Exams: 22% (Exam I) + 30% (Exam II)
- Participation: 8% (online and offline)
- This is a 4 credit graduate level course offered by the Information and Decision Sciences department at UIC.
- Please see the academic calendar for the semester timeline.
- Students who wish to observe their religious holidays (http://oae.uic.edu/religious-calendar/) should notify the instructor within one week of the first lecture date.
- Please contact the instructor at the earliest, if you require accommodations for access to and/or participation in this course.
- Please refer to the academic integrity guidelines set by the university.