Machine Learning
DATA 630 | 6 Credits
Course Desc: Prerequisite: DATA 620. A practical survey of several modern machine learning techniques that can be applied to make informed business decisions. Discussion covers supervised and unsupervised learning techniques, including naïve Bayes, regression, decision trees, neural networks, nearest neighbor, and cluster analysis. How each of these methods learns from past data to find underlying patterns useful for prediction, classification, and exploratory data analysis is examined. Discussion covers significant tasks in real-world applications, including handling of missing data, evaluating classifiers, and measuring precision. Major software tools are used to apply machine learning methods in a wide range of domains such as healthcare, finance, marketing, and government.
