Machine Learning and Statistical Methods for Business Analytics

IDS 575

Overview

The goal of this class is to cover the foundations of modern statistics and machine learning methods 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 will be complemented with hands-on exercises.

Previous Versions

Spring 2019 (has videos!)
Spring 2018 (has videos!)
Fall 2017 (has videos!)

Logistics

Semester: Spring 2022
Lectures: Mondays 3.00 PM to 5.30 PM at DH 210
Staff
- Instructor: Dr. Theja Tulabandhula
- Teaching Assistant: Parshan Pakiman
Offline communication:
- Instructor Office Hours: Thursdays 2.30 to 3.30 PM at UH 2404
- TA Recitations and/or Office Hours: Mondays 6.30 to 7.30 PM on Zoom

Dates

01/10: lecture (online only)
01/24: lecture
01/31: lecture
02/06: assignment 1 due
02/07: lecture (online only)
02/14: lecture
02/20: assignment 2 due
02/21: lecture
02/27: project intermediate report+code due including project plan
02/28: lecture slot for separate team meetings with teaching staff on projects (online only)
03/06: assignment 3 due
03/07: lecture
03/14: lecture
03/27: assignment 4 due
03/28: lecture
04/04: lecture
04/10: assignment 5 due
04/11: lecture
04/17: project final report+code due
04/18: lecture slot for live student project presentations I (online only)
04/28: lecture slot for live student project presentations II (online only)

Materials

Textbook and Slides

Textbook I: Elements of Statistical Learning II.
Textbook II: An Introduction to Statistical Learning with Applications in R.
Slides

Additional Readings

Tentative List of Topics

Supervised Learning (mostly about prediction)

Instance-based Learning
k-Nearest Neighbor Decision Trees
Linear regression, Logistic Regression
Generalized Linear Models
Model Selection and Assessment
Support Vector machines and Duality
Naïve-Bayes and Linear Discriminant Analysis
Hidden Markov Models
Structured Prediction Models
Statistical Learning Theory

Unsupervised Learning (mostly about exploration)

K-means clustering
Mixture of Gaussian
Principal Component Analysis
Independent Component Analysis
Canonical Correlation Analysis

Assignments and Project

Groups

You should form groups of (strictly) 4 students for the assignment and project components. Reach out to your classmates early. Here is a spreadsheet to facilitate this.

Assignments

There will be five assignments, released on Github. These involve reimplementing statistical techniques and understanding their behavior on interesting datasets. Always mention external sources used 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, and no extensions are available. Use Blackboard for uploads. Because this is a group assignment, a commensurate effort is expected, and each member's contributions needs to be reported in the final submission.

Project

The objective will be to demonstrate mastery over data ingestion, processing, prediction modeling and communication of key results. A suitable documentation of this process along with the complete set of scripts/codes/dataset samples/commands used is to be submitted. See the project page for more detailed instructions. Because this is a group project, a commensurate effort is expected, and each member's contributions needs to be reported in the final submission.

Grades

Assignments: 12% + 12% + 12% + 12% + 12%
Project: 5% for intermediate report and 30% for the final (more details in the project page linked above)
Course participation: 5% (includes but is not limited to attendance, interaction with the instructor and the TA, and how well you support your group).

Miscellaneous Information

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.

farhanbadr09 / ml-notebooks Goto Github PK

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