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DATS331 - Machine Learning I

Course Details

Course Code: DATS331 Course ID: 5116 Credit Hours: 3 Level: Undergraduate

This course introduces students to machine learning. If provides students with a broad overview of machine learning topics for both supervised and unsupervised methods. The topics typically include classification, decision trees, association rule-based classification, support vector machines, regression (linear, logistic and Bayesian), clustering, k-Nearest Neighbor, principal component analysis (PCA), Feature Selection, Linear Discriminant Analysis (LDA) and Factor Analysis. Additional topics can include ensemble methods such as stacking, bagging and boosting. (Prerequisite: DATS311)





Prerequisites

Course Schedule

Registration Dates Course Dates Session Weeks
04/26/22 - 09/30/22 10/03/22 - 11/27/22 Fall 2022 Session B 8 Week session

Current Syllabi

Information is provided in the syllabus when the course begins.
Information is provided in the syllabus when the course begins.
Information is provided in the syllabus when the course begins.
Book Title:Machine Learning with R - e-book available in the APUS Online Library
ISBN:9781782162148
Publication Info:Packt Publishing Lib
Author:Lantz, Brett
Unit Cost:$54.99

Previous Syllabi

Not current for future courses.