Nov 28, 2021  
2020-2021 Undergraduate Catalog 
    
2020-2021 Undergraduate Catalog [ARCHIVED CATALOG]

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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.

Prerequisites:
Prerequisites: CS 302 and MATH 260 with a grade of C or higher in each course.

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:
3/6/20

Anticipated Course Offering Terms and Locations:



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