Nov 30, 2022
CS 556 - Data Mining
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.
Prerequisite: CS 529.
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:
Anticipated Course Offering Terms and Locations:
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