Management of AI Transformation Graduate Certificate | Program Details

Introduces students to the foundations and transformative potential of Artificial Intelligence (AI) without requiring programming skills. Students will explore fundamental properties, opportunities, challenges, and limitations of Large Language Models by applying them to common work-related tasks and reflecting on alignment, reliability, explainability, ethics, and workplace impacts. Students will use no-code AI tools for class assignments and a quarter-long applied project, resulting in a functional AI system or AI-enabled workflow. Online, Tuesday 5:30-9:10.

Data has become a key resource for companies and mining it for actionable insights is critical. Methods of data mining ranging from classical multivariate methods to new methods such as machine learning and domains including text mining and social network analysis, are covering with rich cases using a variety of tools. Students can use Python or R in the class and should have a working familiarity with either Python or R. 

Prerequisite: graduate standing or consent of instructor. 

Decision and value theory concepts are applied to technical and management decisions under uncertainty. Multi-criteria decisions are analyzed. Subjective, judgmental values are quantified for expert decisions and conflict resolution in strategic decisions involving technological alternatives. Hierarchical decision modeling approach is introduced. Individual and aggregate decisions are measured. Decision discrepancies and group disagreements are evaluated. Case studies are included in the course. 

Prepares engineers and computer scientists for managing complex technical projects, including manufacturing, construction, and software/hardware. Covers core principles, such as lifecycle, triple constraint (time, cost, performance), stakeholder engagement, project/ matrix organization, and team development. Introduces methods for planning, estimating, scheduling and project tracking, including approaches involving artificial intelligence. Examines how software delivery practices (e.g., Agile and Scrum) impact project management. Provides the basis for skill development via additional coursework and industry/PMI certification. Hybrid, Thursday, 5:30-9:10.

Examines how to start and grow a technology company. Covers the complete venture creation process: key issues in high tech markets, startup finance, growth strategies, and exit strategies. Guest lectures by practicing entrepreneurs, executives and investors. Student teams create a technology startup business around a technology of their choice (not limited to AI), using "lean startup" methodology. Students use AI-based and other tools to research, develop, write, and present their business plan to investors.

This is the same course as ETM 461 and may be taken only once for credit.

Reviews management of technology and innovation towards building a secure cyber space. Focus is on the management aspects highlighting the unique differences of the sector. Several industry executives and academic scholars will be guest lecturers in this class. Students will team up and analyze current cases. 

Also offered for undergraduate-level credit as ETM 480 and may be taken only once for credit. Prerequisite: graduate standing or permission of the instructor. 

Learning Labs are a hands-on and full-day class format, taught on a single Saturday. This term, we will focus on how AI can support product innovation, from idea to tested product concepts. We will apply principles of prompting and agentic AI, using easy-to-use AI tools. No prior experience in AI or product innovation required. Friday, October 16th from 5:30pm-7:30pm (online) & Saturday, October 17th from 9am-3:30pm in-person

Examines the ethical, social, and policy dimensions of artificial intelligence, with attention to fairness, accountability, transparency, privacy, and the societal impacts of automated decision-making. Students analyze real-world cases, evaluate risks, and explore frameworks for responsible AI design and governance. Topics include bias in data and models, algorithmic harm, equity, regulation, and human-AI interaction. Expected preparation: none beyond general familiarity with computing. 

Also offered for undergraduate-level credit at CS 417 and may be taken only once for credit.

Fundamentals of supervised learning and common machine learning models including linear and logistic regression, support vector machines, artificial neural networks, and decision trees/random forests; Hands-on implementation using Python-based tools such as Scikit-Learn, Keras, and TensorFlow.