MATH 342 - Statistical and Machine Learning

2020-2021 Catalog Year

Credit(s): 3
Lecture: 3
Non-Lecture: 0
This course serves as an advanced statistical and algorithmic modeling course. The course includes the processes of model building using two disciplines, statistical learning and machine learning. Emphasis is placed on mathematics and algorithms. The topics include linear and non-linear regression methods, supervised and unsupervised learning methods including industry-standard methods, model improvement and ensemble methods, and handling large data issues. Students will gain mathematical foundations and data science skills with state-of-the-art programming languages such as R and Python, will learn to build high performance predictive models involving real-world data, and will produce a written data analysis report with an oral presentation.

Prerequisite(s): (MATH 230 or MATH 240 or INTD 121 or any 100- or 200- level programming course) and MATH 233 or permission of instructor
Prerequisite(s)/Corequisite(s): MATH 360 or MATH 341
Offered: Not oﬀered on a regular basis