Online Certificate in Artificial Intelligence (Graduate)

Generative Artificial Intelligence for Business

This course examines the technology underlying modern generative artificial intelligence / machine learning models from a business perspective, including their uses in coding, professional and artistic applications, and the various controversies and challenges to work and/or society they may pose. 

Machine Learning Design for Business 

This course provides a framework for developing real-world machine learning systems that are deployable, reliable, and scalable. Designing machine learning systems is the process of defining the software architecture, infrastructure, algorithms, and data for a machine learning system to satisfy specified business requirements. Without deliberate design, machine learning systems can get outdated quickly because (1) the tools continue to evolve, (2) business requirements change, and (3) data distributions constantly shift.
Students will learn about data management, data engineering, feature engineering, approaches to model selection, training, scaling, and how to continually monitor and deploy changes to ML systems for successful business applications. They will also be exposed to managing the human side of ML projects such as team structure and business metrics.

Governance of AI/ML Systems 

This course teaches students how to develop, scale-up, and sustainably manage high-performing Artificial Intelligence/Machine Learning systems in business organizations. It introduces concepts and techniques that enable the development of surrogate approaches to explain AI/ML models, build redundancy in AI/ML systems, and calculate and minimize risk of failures while using such approaches.

Survey of Machine Learning and Artificial Intelligence 

This course is a survey of Machine Learning (ML) and Artificial Intelligence (AI) from the Data Scientist’s perspective.  It explores ML and AI topics, current and emerging technologies, and applications for students to gain understanding of the successful implementation of ML and AI to address key business and industry problems.

Data Wrangling 

This course provides an intensive, hands-on introduction to data management and data manipulation. You will learn the fundamental skills required to acquire, munge, transform, manipulate, and visualize data in a computing environment that fosters reproducibility.

Data Mining I

This is a course in statistical data mining with emphasis on hands-on case study experiences using various data mining/machine learning methods and major software packages to analyze complex real world data. Topics include data preprocessing, k-nearest neighbors, generalized linear regression, subset and LASSO variable selection, model evaluation, cross validation, classification and regression trees.

Data Mining II

This is a course in statistical data mining with emphasis on hands-on case study experiences using various data mining/machine learning methods and major software packages to analyze complex real world data. Topics include advanced trees: bagging, random forests, boosting; nonparametric smoothing methods; generalized additive models; data preprocessing/scaling; neural networks; deep learning; cluster analysis; association rules.

Special Topics in Business Analytics

This course is used to explore topics of current interest in the BANA domain, that do not fall within the scope of any of the regularly scheduled courses. By the nature of the course, specific topics covered will vary with each offering.

Data Mining for Business Intelligence

This course is designed for the in-depth learning of data-mining knowledge and techniques in the context of business intelligence. The topics include association rules, classification, clustering and text mining. Students will apply and integrate the business intelligence knowledge learned in this course in leading software packages. 

Artificial Intelligence-Powered Bots 

This course explores the design, development, and deployment of AI-powered bots, focusing on task automation and conversational AI. Students will learn to automate predefined workflows and gradually incorporate generative AI to enhance bot intelligence and adaptability. Through hands-on projects and case studies, students will gain practical experience in creating AI-powered bots while examining ethical considerations and strategic business impacts.

Big Data Integration

This course presents an overview of the principles of data integration, the fundamental basis for developing useful and flexible business intelligence platforms. Modern data integration needs differ from traditional approaches in four main dimensions that parallel differences between big data and traditional data: volume, velocity, variety, and veracity.

Special Topics in Information Systems 

This course is used to explore topics of current interest in the IS domain, that do not fall within the scope of any of the regularly scheduled courses. By the very nature of the course, specific topics covered will vary with each offering.

Artificial Intelligence in Healthcare Operations

This course explores the transformative role of artificial intelligence (AI) in healthcare, addressing both technical and ethical dimensions. Students will gain an understanding of how AI techniques are revolutionizing patient care, diagnostics, and healthcare management. Through hands-on projects and/or case studies, students will learn to develop, evaluate, and implement AI models tailored to clinical data and healthcare applications. The course will also cover key issues such as data privacy, regulatory frameworks, and the importance of model transparency and fairness in clinical settings. By examining current trends and innovations, including AI in medical imaging, personalized medicine, and public health analytics, students will be equipped to critically assess and contribute to AI advancements that drive better health outcomes. This course is designed for graduate students interested in leveraging AI to address real-world healthcare challenges.

Artificial Intelligence Applications for Supply Chain Management

This course explores how leading firms leverage artificial intelligence (AI) to drive innovation in operations and supply chain management (SCM). The course relies heavily on the case method of instruction. Students will examine AI’s role in the management of operations productivity/efficiency, process management, sourcing/supply management, logistics, and in advancing sustainable practices. Through analysis and discussion of case situations, students will develop a nuanced understanding of how AI shapes modern supply chain management and learn to critically evaluate AI-driven solutions within SCM. By the end of the course, students will be prepared to identify and propose creative use of AI for supply chain decisions and develop a strategic mindset to address the evolving landscape of artificial intelligence as applied to supply chain management. 

Artificial Intelligence in Organizations

This course explores the transformative impact of artificial intelligence (AI) on modern organizations, examining both the opportunities and the challenges that AI presents in the business environment. Students will develop an understanding of AI technologies and their practical applications, while critically analyzing ethical considerations, societal and workforce implications, and strategic implications of AI adoption. The course delves into specific organizational functions, with special emphasis on firm strategy, human resource management, and operational efficiency. Through a combination of theoretical frameworks and real world applications, students will learn to evaluate AI implementation strategies, assess risks, and develop guidelines for responsible AI use in organizations. Students will also gain the knowledge needed to lead AI initiatives, manage human-AI collaboration, and drive innovation in their organizations.

Artificial Intelligence for Marketing Managers

MKTG 7043 is designed to provide the student with a cohesive understanding of how to use various artificial intelligence platforms across the entire continuum of marketing management through the exploration of marketing problems with an emphasis on qualitative and quantitative analysis, integrative marketing decision-making, and strategy formulation.