Project success depends, largely, on the human side. Success in motivating project workers, organizing and leading project teams, communication and sharing information, and conflict resolution, are just a few areas that are critical for project success. However, being primarily technical people, many project managers tend to neglect these “soft” issues, assuming they are less important or that they should be addressed by direct functional managers. The purpose of this course is to increase awareness of project managers to the critical issues of managing people and to present some of the theories and practices of leading project workers and teams.

An interdisciplinary course which examines the elements of, and the framework for, developing and implementing organizational strategy and policy in competitive environments. The course analyzes management problems both from a technical-economic perspective and from a behavioral perspective. Topics treated include: assessment of organizational strengths and weaknesses, threats, and opportunities; sources of competitive advantage; organizational structure and strategic planning; and leadership, organizational development, and total quality management.

The study of marketing principles from the conceptual, analytical, and managerial points of view. Topics include: strategic planning, market segmentation, product life-cycle, new product development, advertising and selling, pricing, distribution, governmental, and other environmental influences as these factors relate to markets and the business structure.

This course covers foundational principles that drive machine learning applications and provides practice implementing algorithms. Topics include maximum likelihood estimation, dimension reduction, supervised and unsupervised learning, neural networks, and non-parametric methods. Students gain tools to address new ML problems, applying techniques such as regression, SVMs, decision trees, clustering, and backpropagation, with an emphasis on practical problem-solving using software libraries and real data.

Artificial Intelligence (AI) is an interdisciplinary field that draws on insights from computer science, engineering, mathematics, statistics, linguistics, psychology, and neuroscience to design agents that can perceive the environment and act upon it. This course surveys applications of artificial intelligence to business and technology in the digital era, including autonomous transportation, fraud detection, machine translation, meeting scheduling, and face recognition. In each application area, the course focuses on issues related to management of AI projects, including fairness, accountability, transparency, ethics, and the law.

This course explores the integration of augmented intelligence, generative AI, natural language processing, and deep learning in data-driven business contexts. Students gain foundational and practical knowledge in large language models (LLMs), prompt engineering, and AI ethics. A major team project involves developing a proof-of-concept business solution leveraging generative AI to create measurable value.