CS 456 - 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:
Prerequisites: CS 302 and CS 361 and MATH 260.

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