Nov 30, 2022  
2019-2020 Graduate Catalog 
    
2019-2020 Graduate Catalog [ARCHIVED CATALOG]

Add to Portfolio (opens a new window)

CS 556 - Data Mining


Description:
Introducing concepts, models, algorithms, and tools for solving data mining tasks; decision trees, time series, Bayesian methods, k-nearest neighbors, and relational databases. CS 456 and CS 556 are layered courses; students may not receive credit for both. Course will be offered every year. Course will not have an established scheduling pattern.

Prerequisites:
Prerequisite: CS 529.

Credits:
(4)

Learner Outcomes:
Upon successful completion of this course, the student will be able to:

  • Characterize specific data mining tasks, introducing concepts, models, algorithms, and tools for solving data mining tasks; decision trees, time series, Bayesian methods, k-means, k-nearest neighbors, and relational databases–from “decision trees”.
  • Use machine learning algorithms to solve data clustering and classification problems.
  • Identify the important of data mining in financial applications.

Learner Outcomes Approval Date:
5/17/18

Anticipated Course Offering Terms and Locations:



Add to Portfolio (opens a new window)