MSAN 621 - Introduction to Machine Learning

Credits: 2

This course focuses on the core theory and application of classification and clustering techniques, feature selection, and performance evaluation. Algorithms discussed include logistic regression, support vector machines (SVM), k-Nearest Neighbors (kNN), Naive Bayes, association rules (a priori algorithm), decision trees, neural networks, clustering, and ensemble methods. Using tools available in Python and R, students will gain experience with application of the theory to key predictive and descriptive analytics problems in business intelligence. Special attention is drawn to practical issues such as class imbalance, noise, missing data, and computational complexity.

Restriction: Restricted to Graduate level; Restricted to Analytics Majors SC