GRAD 5300 – Foundations of Artificial Intelligence – 3 Credits: LISTED IN PROPOSAL BUT NOT IN GRADUATE CATALOG

OPIM 5509 – Introduction to Deep Learning – 3 Credits: Introduction to topics related to deep learning and will build on your previous experience in predictive analytics. Use of neural networks for a host of data and applications – including time series data, text data, geospatial data, and image data.

OPIM 5515 – Generative AI for Business – 3 Credits: A focused course blending foundational knowledge and practical application of generative AI in business. It begins with an overview of generative AI architecture and its business applications, including essential skills in prompt engineering for effective AI model interaction. The course then advances to cover Retrieval-Augmented Generation (RAG) and agentic systems, tailoring AI to specific industry needs while emphasizing ethical and responsible AI usage. Designed for participants with a basic understanding of AI or relevant business technology experience, this course offers a hands-on approach, preparing students to skillfully integrate generative AI into their business strategies and operations.

OPIM 5517 – Building Advanced Generative AI Systems – 3 Credits: Hands-on coding skills necessary to design, implement, and deploy advanced Generative AI systems. Emphasizes the technical development of AI architectures and coding proficiency, with a focus on foundational and advanced topics, including Retrieval-Augmented Generation (RAG), agentic systems, and model fine-tuning. Using Python and Jupyter Notebooks, students will develop and iterate upon real-world generative AI applications, progressing from building RAG models to fine-tuning and deploying AI systems in production. Key technical areas include embedding techniques, vector database integration, prompt engineering, and establishing agentic workflows. Balances coding exercises with project-based learning to reinforce the skills needed to engineer high-performing Generative AI systems.

OPIM 5518 – AI Governance: A Risk Management Framework for Trustworthy and Responsible AI – 3 Credits: Explores the principles, practices, and frameworks of AI governance. Students will learn how to align AI applications with ethical, regulatory, and organizational objectives to ensure accountability, transparency, and trustworthiness. Topics include regulatory compliance, risk management, ethical considerations, and the societal impact of AI in business decision-making. It will benefit students in business analytics, data scientists, MBA, policy and legal, professionals in regulated industries, AI enthusiasts, and social impact advocates.

OPIM 5603 – Statistics in Business Analytics – 3 Credits: Advanced level exploration of statistical techniques for data analysis. Students study basic concepts in descriptive and inferential statistics, data organization and visualization, sampling, probability, random variables, sampling distributions, hypothesis testing, linear regression, and logistic regression. Topics will focus on rigorous statistical estimation and testing. Prepares students with the skills needed to work with data using analytics software.

OPIM 5604 – Predictive Modeling – 3 Credits: Introduces the techniques of predictive modeling in a data-rich business environment. Covers the process of formulating business objectives, data selection, preparation, and partition to successfully design, build, evaluate and implement predictive models for a variety of practical business applications. Predictive models such as neural networks, decision trees, Bayesian classification, and others will be studied. The course emphasizes the relationship of each step to a company’s specific business needs, goals and objectives. The focus on the business goal highlights how the process is both powerful and practical.