8 courses with the subject AAI, each shown exactly as we captured it from the college's catalog, with every element we hold. Where the wording looks broken, that is our reading of the catalog, not the college's text.
AAI 101Introduction to Artificial Intelligence3
This course provides an introduction to the fundamental principles, concepts, ethics, and applications of Artificial Intelligence (AI). Students will explore the historical development of AI, gaining a foundational understanding of core AI algorithms and machine learning techniques. Through hands-on projects students will gain practical experience in developing simple AI applications. This course serves as a foundation for further study in the field of Artificial Intelligence.
As AI permeates society, the ethical considerations of its deployment become paramount. This course explores the social impact of AI, focusing on algorithmic bias, data privacy, and the displacement of labor. Students will analyze real-world examples of AI “failures” to understand the moral responsibilities of those who design and implement these systems. The curriculum emphasizes the development of “Ethical Frameworks” to guide AI governance in professional settings.
This course provides a foundational exploration of how data is used to drive strategic business decisions. Students will learn the “Analytics Lifecycle,” from data collection and cleaning, to descriptive and diagnostic analytics. Using industry-standard tools like Excel and PowerBI, the curriculum focuses on identifying patterns in historical data to explain the gap between raw data and actionable business intelligence.
This course focuses on the “Augmented Professional,” teaching students how to integrate AI into their daily workflows to enhance efficiency and quality. Students will master advanced techniques
This course transitions from descriptive analytics to the “Predictive” phase of AI. Students will learn how to build and evaluate models that forecast future trends in sales, customer behavior, and financial markets. Key topics include linear and logistic regression, time-series analysis, and the evaluation of model accuracy. Using software like Excel and basic Python-based tools, students will learn how to turn historical data into a roadmap for the future.
This course provides a high-level technical overview of Machine Learning algorithms without requiring a deep computer science background. Students will explore Supervised, Unsupervised, and Reinforcement Learning, focusing on how these algorithms are used to automate complex business decisions. The course emphasizes the manager’s role in selecting the right algorithm and ensuring the “Explainability” of automated decisions.
AI and analytics are only as effective as the insights they inspire. This course focuses on the “Final Mile” of the analytical process: turning complex AI outputs into persuasive executive insights. Students will learn to apply data visualization, the story from data, and the art of the presentation. The curriculum emphasizes how to build a data-driven narrative that moves a C-Suite audience to action. Students will master tools for advanced visualization and the creation of “Executive
This course serves as the culminating experience for the degree, requiring students to integrate and apply a comprehensive suite of AI and business analytics tools to a significant, real-world project. Students will partner with external organizations, faculty programs, campus offices/ centers, and other approved learning environments under the direction of faculty to engage in
