Course Information:

  • Course: IDS 472 - Data Mining for Business
  • Term: Fall 2018
  • Time: Tue/Thu 12:30pm - 1:45pm
  • Location: Burnham Hall | Room 317
  • Contact: yuhenghu at uic dot edu
  • TA Contact: Gizem Atasoy ([email protected]), Thusday 2:00 - 3:00 in DH 2401

Overview

It becomes increasingly critical to mine high-quality information from the data. The mined patterns from data are import for many applications, including business intelligence, information acquisition, behavior analysis and decision making. In this course, we will cover several important topics in data mining including: classification, regression, and clustering. We will also provide opportunities to gain hands-on experience of handling large-scale data set.

Academic Integrity

You are expected to adhere to the highest standards of academic honesty. Unless otherwise specified, collaboration on assignments is not allowed. Use of published materials is allowed, but the sources should be explicitly stated in your solutions. Violations will be reviewed and sanctioned according to the University Policy on Academic Integrity. Collaborations among team members are only allowed for the final term projects that are selected. "Academic integrity is the pursuit of scholarly activity free from fraud and deception and is an educational objective of this institution. Academic dishonesty includes, but is not limited to, cheating, plagiarizing, fabricating of information or citations, facilitating acts of academic dishonesty by others, having unauthorized possession of examinations, submitting work for another person or work previously used without informing the instructor, or tampering with the academic work of other students." For more information about violations of academic integrity and their consequences, consult http://vcsa.uic.edu/

Recommended textbooks

Schedule

Week 1 Introduction
Week 2 Data
Week 3 Data Preprocessing
Week 4 Classification I
Week 5 Classification II
Week 6 Classification III
Week 7 Classification IV
Week 8 Evaluation of Classifiers
Week 9 Regression analysis
Week 10 Mid-term
Week 11 Clustering
Week 12 Clustering
Week 13 Associate Analysis and False Discovery
Week 14 Other topics
Week 15 Final Presentation