Data Mining

Catalog description

This course introduces the principles, fundamental algorithms, and applications of Machine Learning. Students will explore supervised and unsupervised machine learning techniques, including regression, classification, clustering, dimensionality reduction, and ensemble methods. Emphasis will be placed on understanding the theoretical foundations of model evaluation, validation, and the ethical implications of automated decision-making. Students will gain hands-on experience implementing and evaluating machine learning models using real-world datasets with Python using popular libraries such as scikit-learn, pandas, and TensorFlow. Prerequisite(s): CIS 3302 or CIS 4301 or (CIS 3310 and CIS 4315).<br/>