IDS 575 Statistical Models and Methods for Business Analytics
Edition: Fall 2017
Document version: Oct 16 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
- Lectures: Saturdays 9.30 AM to 12.00 noon at Lecture center building C C001
- Instructor: Dr. Theja Tulabandhula
- Teaching Assistants:
- Online communication:
- Offline communication:
- Instructor Office Hours: Saturdays 12.00 noon - 1.00 PM or schedule by email (theja at uic)
- TA Office Hours (Parshan): Fridays 5.00 PM to 7.00 PM at UH2432 or schedule by email (ppakim2 at uic)
- TA Office Hours (Haesung): Thursdays 4.00 PM to 6.00 PM at UH2432 or schedule by email (hkim379 at uic)
Textbook and Materials
Software
Timeline
Lectures (tentative)
- 09/02 : Supervised Learning: Linear Models and Least Squares, k-Nearest Neighbor Methods (video)
- 09/09 : Towards Regression: Statistical Decision Theory, Curse of Dimensionality, Linear Regression, Categorical Variables, Interaction Terms (video)
- 09/16 : Regression I: Bias-variance Trade-off, Subset Selection, Cross-Validation (video)
- 09/23 : Regression II: Ridge Regression, LASSO (Least Absolute Shrinkage and Selection Operator) (video)
- 09/30 : Classification, Model Assessment and Selection: Linear Discriminant Analysis, Logistic Regression, AIC, BIC and Validation (video)
- 10/07 : The Bootstrap and Maximum Likelihood Estimation (video)
- 10/21 : Expectation Maximization and Sampling (Markov Chain Monte Carlo) (video)
- 10/28 : Tree Methods, Adaboost and Gradient Boosting (video)
- 11/4 : Random Forests, Multivariate Adaptive Regression Splines and Support Vector Machines (video)
- 11/11 : Introduction to Unsupervised Learning: Clustering (video)
- 11/18 : Unsupervised Learning: Principal Component and Factor Analysis (video)
- 12/02 : Time Series Analysis (video)
Assignments
- 09/09: Assignment 1 out. Due on 09/22
- 09/23: Assignment 2 out. Due on 10/06
- 10/28: Assignment 3 out. Due on 11/10
- 11/11: Assignment 4 out. Due on 11/22
Exams
- 10/14: Midterm Exam (same venue as lectures, and during class hours)
- 12/09: Final Exam (same venue as lectures, and during class hours)
Project
- 10/20: Project first report due (proposal)
- 11/17: Project mid report due
- 12/01: Project final report due
Note: Submission deadline for assignments and project reports is before 2359hrs on the concerned day. Use Blackboard for uploads. Here is a Google Calendar that reflects the same information.
Grades
- Assignments (4): 10%+10%+10%+10%
- Exams (2): 20% (Midterm) + 20% (Final)
- Project (3): 5% (First) + 5% (Mid) + 10% (Final)
Assignments
- Always mention sources in your assignment solutions and project writeups.
- Late submissions will have an automatic 20% penalty per day.
Exams
- These are closed book, but one 8.5x11-inch cheatsheet is allowed.
- No computers and communication devices are allowed.
Project
- This involves working on and documenting a machine learning problem 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.
Miscellaneous Information
- This is a 4 credit graduate level course with CRN 37447, offered by the Information and Decision Sciences department at UIC.
- The semester runs from Aug 28, 2017 - Dec 08, 2017 (academic calendar).
- Students who wish to observe their religious holidays (http://oae.uic.edu/religious-calendar/) shoud notify the instructor by Sept 10.
- 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.