IDS 575 Statistical Models and Methods for Business Analytics
Edition: Spring 2018
Document version: Mar 19 2018
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.
- Lectures: Saturdays 9.30 AM to 12.00 noon at Douglas Hall 220
- Optional Recitations: Select Saturdays 1.00 PM to 2.00 PM at Douglas Hall 220 (check Slack)
- Online communication: Slack
- Offline communication:
- Instructor Office Hours: Saturdays 12.00 noon - 1.00 PM (DH 220) or schedule by Slack
- TA Office Hours: Thursdays 4.00 PM to 6.00 PM at UH2432 or schedule by Slack
Textbook and Materials
- 01/20 : Supervised Learning: Linear Models and Least Squares, k-Nearest Neighbor Methods
- 01/27 : Towards Regression: Statistical Decision Theory, Curse of Dimensionality, Linear Regression, Categorical Variables, Interaction Terms
- 02/03 : Regression I: Bias-variance Trade-off, Subset Selection, Cross-Validation
- 02/10 : Regression II: Ridge Regression, LASSO (Least Absolute Shrinkage and Selection Operator)
- 02/17 : Classification: Linear Discriminant Analysis, Logistic Regression, Model Assessment and Selection: AIC, BIC and Validation
- 02/24 : The Bootstrap and Maximum Likelihood Estimation
- 03/10 : Expectation Maximization and Sampling (Markov Chain Monte Carlo)
- 03/17 : Tree Methods, Adaboost and Gradient Boosting
- 03/24 : Random Forests, Multivariate Adaptive Regression Splines and Support Vector Machines
- 04/07 : Kernel Trick, Introduction to Unsupervised Learning, Association Rules
- 04/14 : Unsupervised Learning: Clustering, Principal Component Analysis and Spectral Clustering
- 04/21 : Time Series and Supervised Learning, The ARMA Model
- 04/28 : Project Presentations
(A concatenated set of scribed notes are available here)
- 01/27: Assignment 1 out. Due on 02/12 0759hrs
- 02/10: Assignment 2 out. Due on 02/26 0759hrs
- 03/24: Assignment 3 out. Due on 04/23 0759hrs
- 03/03: Exam I (same venue as lectures, and during class hours)
- 05/05: Exam II (same venue as lectures, and during class hours)
- 03/19 0759hrs: Project Report I due
- 04/30 0759hrs: Project Report II due
- Assignments (3): 10% + 10% + 10%
- Exams (2): 20% (Exam I) + 25% (Exam II)
- Project (2): 7% (Report I) + 18% (Report II)
- Always mention sources in your assignment solutions and project writeups.
- Late submissions will have an automatic 20% penalty per day.
- These are closed book, but one 8.5x11-inch handwritten cheatsheet is allowed.
- No computers and communication devices are allowed.
- This involves working on and documenting a machine learning solution on a dataset of your choice (e.g., reimplementing and verifying the results of any research paper appearing in recent machine learning and data mining conferences). See details on Blackboard.
- This is a 4 credit graduate level course with CRN 38069, offered by the Information and Decision Sciences department at UIC.
- The semester runs from Jan 16, 2018 - May 04, 2018 (academic calendar).
- Students who wish to observe their religious holidays (http://oae.uic.edu/religious-calendar/) should notify the instructor by Jan 20.
- 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.