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Course Syllabus

MATH 3480 Machine Learning

  • Division: Natural Science and Math
  • Department: Mathematics
  • Credit/Time Requirement: Credit: 3; Lecture: 3; Lab: 0
  • Prerequisites: Math 3080 and (either Math 2270 or Math 2250) with a C or better in each course. Some familiarity with a program language including a basic understanding of data structures and algorithms.
  • Semesters Offered: Spring
  • Semester Approved: Summer 2020
  • Five-Year Review Semester: Fall 2025
  • End Semester: Spring 2026
  • Optimum Class Size: 20
  • Maximum Class Size: 25

Course Description

This course introduces the theory and application of machine learning, sometimes referred to as artificial intelligence. Students who take this course will understand and be able to deploy basic supervised and unsupervised learning techniques including” decision trees, neural networks, kernel methods, support vector machines, and probabilistic methods. The course will be taught using Python, R, Matlab, or a similar programming language.

Justification

Data collection and the analysis of data is ubiquitous and fast becoming a prerequisite to economic success for businesses. This course is a necessity for advanced data science.

Student Learning Outcomes

  1. Students will understand the basic data structures in Python (or similar software)
  2. Students will understand how to visualize, explain, and present data using Python (or similar software)
  3. Students will understand how to employ common machine learning libraries in a popular computing language (e.g., Python, R, etc).
  4. Students will understand the mathematical theory behind common machine learning techniques.

Course Content

This course will include several fundamental supervised/unsupervised learning algorithms including decision trees, perceptrons, neural networks, kernel methods, support vector machines, and probabilistic methods like Bayesian networks.