This course examines the intersection of regulatory compliance, providing students with an understanding of how to implement AI technologies responsibly within the bounds of laws and ethical standards. Participants will explore both the technical aspects of AI frameworks and the regulatory landscape affecting their deployment in various industries.

This course provides an in-depth exploration of deep learning, a subset of machine learning that focuses on neural networks with many layers
(deep architectures). Students will learn the theory, methodologies, and practical implementations of deep learning model.

This course offers a comprehensive study of neural networks and large-scale learning models, emphasizing their architecture, training methodologies, and practical applications. Participants will explore advanced neural network designs, including their scalability and efficiency in processing large datasets.

A capstone is a project in which students may culminate their learning by completing a research project under the supervision of a faculty adviser. In most cases, students work on a capstone either individually and students may partner with an external company, or work on a project associated with their employer as result that is provided to the company. For projects that work with an external organization, the student needs to review the Disclaimer requirements and receive permission from the external partner. A grade of "B" is required to complete the graduate degree.