Applied Machine Learning

(1.5 Credit Hours) · Undergraduate

This course introduces the core concepts, methods, and applications of machine learning, a central component of modern artificial intelligence and data-driven systems. Students learn how algorithms identify patterns in data and make predictions or decisions without explicit rule-based programming. The course covers foundational machine learning approaches, including supervised and unsupervised learning, with emphasis on model selection, evaluation, and best practices for responsible deployment. Students examine how machine learning systems are trained, validated, and used in real-world settings, and explore issues related to AI safety, ethics, bias, and transparency. Through applied examples and case studies drawn from a range of domains, students develop an understanding of how machine learning supports automation, prediction, and intelligent decision making. The course prepares students to engage critically and practically with AI-enabled technologies across analytics, strategy, and technology-driven organizations. Students are strongly encouraged to complete ITAO 40230 before enrolling in this course.