IS 733: Data Mining — Spring 2021
Times: Tuesday 7:10pm – 9:40pm
Location: Online (Blackboard Collaborate)
Instructor: Nirmalya Roy
Instructor’s Office Location and Hours: ITE 421 Tuesday 9:00 am – 10:00 am, or by appointment
Instructor’s Email: nroy at umbc dot edu
Data Mining: Practical Machine Learning Tools and Techniques, Fourth Edition (Witten et al.) is the primary textbook. You will need this book for course readings. Until you obtain it, the UMBC library has an electronic copy for online access that you can use. Earlier editions of the textbook are acceptable but not recommended. Some material will be missing, and it will be up to you to convert chapter/section/page numbers to the older edition for the required readings.
Homework and Exam Policies:
Course overview, introduction to data mining
Applications, the data mining process, data mining ethics
Usama Fayyad, Gregory Piatetsky-Shapiro, and Padhraic Smyth, From Data Mining to Knowledge Discovery in Databases, AI Magazine 17(3) 1996, up to page 7. Optional alternative reading: Witten et al., Ch 1.
Data cleaning, integration, transformation, reduction, discretization. Principal components analysis.
OLAP vs OLTP, data cubes. Sharing project ideas
Project Proposal due
Linear models, trees, rules.
HW2 due (extended to March 5)
Supervised learning (continued)
Evaluation of supervised learning
Hold-out method, cross validation, ROC curves
HW3 due on April 2 (extended)
Association rule learning, K-means, hierarchical clustering
Bryan Dilone and Brahmani Thota
The Study of Opinion Mining on Amazon Electronics
Amazon’s product data
Len Mancini and David Fahnestock
Study the impact of COVID on ridership and rider patterns in micro-mobility
Geetham Godavarthi and Rajashekara Chennareddy and Supriya Gadhiraju
Stock Market Analysis
Karan Dhamecha and Namrata Kelkar and Sree Purani
Predicting Fetal Risk Using Cardiotocographic Data
Nathan Antonicelli and Monica Lingham
Sentiment analysis in Politics
Leslie Leslie and Guy-Alain Aurelien
Free Vacation, Click Here!
Ira Winkler and Henry Ejim
Phishing Susceptibility By Message Lure Type