Introduction to Machine Learning and Big Data Analytics. Topics include: Association Rule Mining, Classification, Clustering, Linear and Logistic Regression, Distributed File System, Batch and Stream Data Processing, and other related. Applications on other domains such as multimedia, networks, finance, and/or business. Includes: Experiential Learning Activity Prerequisite(s): IRM 3006. Lectures three hours a week. [0.5 credits]
Introduction to Machine Learning and Big Data Analytics. Topics include: Association Rule Mining, Classification, Clustering, Linear and Logistic Regression, Distributed File System, Batch and Stream Data Processing, and other related. Applications on other domains such as multimedia, networks, finance, and/or business. Includes: Experiential Learning Activity Prerequisite(s): IRM 3006. Lectures three hours a week. [0.5 credits]
