Online Master of Science in Enterprise AI

This module orients learners to navigate a programming environment setup and tools like the filesystem, command line, and notebooks. It also includes a review of fundamental components of the Python programming language, including data structures such as scalars, vectors, arrays, and dictionaries; installing and importing packages; control flow; loops; and functions.

This module explores essential concepts and techniques in machine learning, including key topics such as linear methods, including linear regression, lasso, and ridge; tree methods, including decision trees, random forests, and boosting; and unsupervised methods like clustering.

Learners gain proficiency fitting various machine learning models, understanding regularization to prevent overfitting, and using cross-validation to evaluate model performance and tune hyperparameters.

This module provides a comprehensive exploration of data handling techniques, leveraging case studies and real-world examples. It begins with basic SQL syntax and data manipulation and progresses to advanced SQL topics, including SELECT statements, filtering, sorting, aggregation, grouping, and joins.

Learners gain proficiency in modifying data and working with database indexing, constraints, and views in SQL.

This hands-on course focuses on the complete toolkit for customizing large language models. Students master the three core methods for adapting LLMs to enterprise tasks: prompt engineering, Retrieval-Augmented Generation, also known as RAG, and fine-tuning.

This course teaches students how to operationalize AI by deploying, scaling, and maintaining models in a production environment. Topics include core MLOps practices, containerization with Docker, automated CI/CD pipelines, and orchestration with Kubernetes.

This module explores ethical considerations inherent in data-driven decision-making and the deployment of algorithmic systems. It emphasizes the socially constructed nature of science, shedding light on biases and inequalities.

Learners explore the societal and cultural implications of AI and machine learning technologies, analyzing potential biases and disparities in data and algorithms. Practical skills are developed through the application of fairness metrics to evaluate and mitigate bias in algorithmic decision-making.

Privacy, ethical considerations surrounding personal data, and navigating key regulatory frameworks related to AI and machine learning, such as GDPR, the California Consumer Privacy Act, and other data privacy laws, are integral components.

This course focuses on building autonomous AI agents that can reason, plan, and use tools to solve complex business problems. Students use frameworks like LangGraph to create and deploy sophisticated agents that automate enterprise workflows.

This course focuses on the business leadership of AI. Students learn to identify high-value AI opportunities, build a compelling business case, formulate an enterprise-wide AI strategy, and design effective human-AI collaboration workflows to drive transformation.

The course also introduces the principles of experimental design and A/B testing to rigorously measure the causal impact of AI interventions. It examines the broader economic impacts of AI to inform long-range planning.

This program-long, hands-on sequence of courses allows students to apply skills from all courses to solve a real-world enterprise problem. The capstone runs across all four semesters, moving from proposal and design to final prototype and presentation.

The course also includes a speaker series.