Machine Learning

Catalog description

This course provides a thorough introduction to machine learning, covering theory and practical implementation. It includes regression, classification, clustering, and Markov decision processes, exploring topics like linear and logistic regression, regularization, Bayesian inference, SVMs with kernel methods, ANNs, clustering, and dimensionality reduction. The goal is to equip students with the essential methodologies, technologies, mathematical principles, and algorithms for real-world machine learning applications. Prerequisite Course(s): Prerequisites: CSCI 425, CSCI 435 Classroom Hours - Laboratory and/or Studio Hours – Course Credits: 3-0-3