DATS331 - Machine Learning I
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)
|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|
|Book Title:||Machine Learning with R - e-book available in the APUS Online Library|
|Publication Info:||Packt Publishing Lib|
Not current for future courses.