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Course Syllabus

Course: MATH 3480

Division: Natural Science and Math
Department: Mathematics
Title: Machine Learning

Semester Approved: Summer 2020
Five-Year Review Semester: Fall 2025
End Semester: Spring 2026

Catalog 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.

Semesters Offered: TBA
Credit/Time Requirement: Credit: 3; Lecture: 2; Lab: 0

Prerequisites: Math 3000 and (Math 2270 or Math 2250) with a C or better course grade. Some familiarity with a program language including a basic understanding of data structures and algorithms.

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:
Students will understand the basic data structures in Python (or similar software) Students will be assessed through assignments, quizzes, exams and/or class discussion – instructor will provide feedback.

Students will understand how to visualize, explain, and present data using Python (or similar software) Students will be assessed through assignments, quizzes, exams and/or class discussion, and projects – instructor will provide feedback.

Students will understand how to employ common machine learning libraries in a popular computing language (e.g., Python, R, etc).  Students will be assessed through assignments, class projects, quizzes, exams and/or class discussion – instructor will provide feedback.

Students will understand the mathematical theory behind common machine learning techniques. Students will be assessed through assignments, quizzes, exams and/or class discussion – instructor will provide feedback.


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.

Key Performance Indicators:
Student learning will be evaluated through:

Homework assignments  20 to 40%

Quizzes 0 to 20%

Periodic examinations 20 to 30%

Final Exam 15 to 20%

Oral/written/computer projects 10 to 30%

Class group activities (optional) 0 to 15%


Representative Text and/or Supplies:
VanderPlas, J. Python data science handbook: Essential tools for working with data. Sebastopol, CA: OReilly.

Kutz, J. N. Data-driven modeling et scientific computation: Methods for complex systems et big data. Oxford: Oxford Univ. Press.

Grus, J. Data science from scratch: first principles with Python. Sebastopol (CA): OReilly Media.

Computational software: Python, R, Matlab, etc.


Pedagogy Statement:
John Dewey’s stated that “education should not revolve around the acquisition of a pre-determined set of skills, but rather the realization of one’s full potential and the ability to use those skills for the greater good.” Applying this idea to the pedagogy of this course, the teacher will help students learn both theory and application in a modern curriculum. By the end of the course, students should know how to use technology to apply specific skills and to analyze the results of their work.

Instructional Mediums:
Lecture

Maximum Class Size: 25
Optimum Class Size: 20