Semester:
Fall of every year
Credits:
Total Credits: 3 Lecture/Recitation/Discussion Hours: 3
Recommended Background:
Programming skills in C, C++, Java and Matlab. Basic knowledge in calculus, probability and statistics.
Description:
Techniques and algorithms for knowledge discovery in databases, from data preprocessing and transformation to model validation and post-processing. Core concepts include association analysis, sequential pattern discovery, anomaly detection, predictive modeling, and cluster analysis. Application of data mining to various application domains.
Semester:
Spring of every year
Credits:
Total Credits: 3 Lecture/Recitation/Discussion Hours: 3
Prerequisite:
CSE 840 or CSE 482
Recommended Background:
Programming skills in C, C++, Java and Matlab. Basic knowledge in calculus, probability and statistics.
Restrictions:
Open to graduate students in the Department of Computer Science and Engineering or approval of department.
Description:
Techniques and algorithms for knowledge discovery in databases, from data preprocessing and transformation to model validation and post-processing. Core concepts include association analysis, sequential pattern discovery, anomaly detection, predictive modeling, and cluster analysis. Application of data mining to various application domains.