Fall 2017 CISC4631: Data Mining

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General Information

Instructor: Dr. Yijun Zhao
Email: yzhao@cis.fordham.edu
Lecture Time: Tuesday & Friday 10:00am - 11:15am Leon Lowenstein Bldg. 520
Office Hours (primary): Friday 4:00pm - 5:30pm Leon Lowenstein Bldg. 305
Office Hours (secondary): Thursday 4:00pm - 5:00pm Leon Lowenstein Bldg. 812
Other Q&A Resource: peer-based Q&A available via Piazza. Signup link: http://piazza.com/fordham/fall2017/cisc4631l01


Jiawei Han, Micheline Kamber, and Jian Pei., Data Mining: Concepts and Techniques, 3rd edition, Morgan Kaufmann, 2011

Recommended books for further reading:

Learning from Data by Yaser S. Abu-Mostafa, Malik Magdon-Ismail and Hsuan-Tien Lin, AMLBook (2012)
Pattern Recognition and Machine Learning by Christopher M. Bishop, Springer (2011)


Description of Course

This course introduces concepts, algorithms, and techniques of data mining as well as the practical issues that arise when applying these algorithms to real-world problems. The students will learn various aspects of data mining, including classification, prediction, ensemble methods, association rules, sequence mining, time series mining and cluster analysis. The homework assignments consist of both theory (written) and programming components. The class project involves building a predictive model using a real-world large data sets.


The students are expected to:

Course Outline (Topical):


Course Project

There will be a course project to predict an individual's income level using data extracted from the census bureau database. The project can be completed either individually or as a group (strongly recommended). A group can have at most 4 people. More details will be discussed in class.




Additional Remarks

Last modified: Aug. 28, 2017