Course Syllabus

MATH 3280 Data Mining

Course Description

This course provides an overview of handling large datasets and how to measure similarities between them. Students will learn techniques for measuring similarities between datasets and using them to make recommendations to individuals and society. Students will also learn the different classes of machine learning models and how to evaluate model performance.

Justification

Data collection and the analysis of data is ubiquitous and is quickly becoming an essential element of economic success for businesses. MATH 3280 is the 2nd in a 3-course series on Data Science. This course focuses primarily on handling large datasets, including measures of similarity between large datasets. The second half of the course introduces students to important techniques used in machine learning models, preparing them for the 3rd course in the series. This course will support the bachelor's in software engineering degree by providing relevant mathematics coursework.

Student Learning Outcomes

1. Students will be able to discuss issues involved with large datasets and solutions to handling and processing large datasets
2. Students will be able to use a variety of techniques to find similarities between large datasets
3. Students will be able to explain the differences between types of machine learning models
4. Students will be able to use a number of mathematical processes and models to predict the behavior of large datasets
5. Students will be able to use evaluation methods to determine the accuracy of models

Course Content

This course will focus on methods to handle large datasets. Topics may include similarity search, graph analysis, and PageRank. Students will be introduced to the different classes of machine learning models and learn how to use some of these models. Evaluation techniques and improvement methods are also addressed in this course. Discussions of other models are continued in MATH 3480 Machine Learning.