AI Engineering, Software Engineering Master's Degree

Real Life Applications of Data Structures covers basic to advanced topics in data structures and their associated algorithms with a focus on real world examples and applications. This course focuses on problem-solving techniques for designing efficient and maintainable software solutions using Python.

Advanced Software Engineering delves into the intricate processes and methodologies essential for developing sophisticated, scalable software architectures. This course offers an in-depth exploration of the software engineering lifecycle, emphasizing a disciplined approach to navigating the complex challenges of software engineering. Students will explore advanced process frameworks and methodologies, including the Waterfall Model and Agile Development, tailored to large-scale and high-stakes projects.

Software Product Design and Requirement Engineering provides an in-depth exploration into effectively integrating user needs and system requirements into software development. This course covers topics from types of requirements, requirement gathering techniques, prioritization, and documentation.

This course covers topics in designing, analyzing, and managing large-scale software systems. Students will learn various architecture types, how to select and implement appropriate design patterns, and how to build well-structured, reliable, and secure software systems.

This course equips students with the skills and knowledge necessary for ensuring high-quality software development and effective deployment practices. The curriculum covers quality models and metrics, QA methods, automated testing, and the design and implementation of test cases. This course introduces students to continuous integration/continuous deployment (CI/CD) pipelines and tools, performance testing, and the tools required for assessing software performance. Students will also learn about deployment strategies, rollback procedures, disaster recovery plans, and monitoring and logging practices, each crucial for the software's operational integrity in production environments.

In this course, students are equipped to understand, analyze, and implement cloud computing solutions tailored to diverse business needs. The curriculum offers an in-depth look at state-of-the-art cloud technologies, the underlying business trends fueling cloud adoption, and the foundational systems of modern data center computing. Participants will learn to identify and apply various cloud systems to specific business cases, understand the interplay between network systems to form cloud computing environments, and utilize cloud computing models to address infrastructure challenges. A focus on network security enables learners to assess and enhance network security systems, covering cybersecurity principles, perimeter security, authentication, and the creation of secure networks. The course also delves into scalable distributed systems and programming frameworks that facilitate edge computing, illustrating how cloud-based solutions can resolve real-world business problems.

This course is designed to familiarize students with machine learning through the lens of a software engineer. Students will explore the challenges and opportunities of applying machine learning to solve problems and create strategic objectives. Students will explore various industries and learn how to apply machine learning to address business needs. While in this course, students will demonstrate how to effectively communicate their recommendations to a range of stakeholders.

Integrating AI with Modern Software Applications prepares students to analyze and explain various technical and analytical components of machine learning models. Students will explore the design of system architectures to scale, while looking at potential risks and mitigation strategies for integrating AI into existing software applications. The content in this course will give the student the ability to understand how to describe data used by AI systems, its sources, and characteristics, as well as instructing on methods for deployment and monitoring.

This course provides students with the opportunity to learn about how AI systems are designed to align with human behavior and values. Students will characterize human centered design as it relates to AI, while analyzing the ethical implications of AI solutions on both individuals and society. Students gain insights into the impact of AI on humans and human values. Finally, students will understand and explain how to incorporate mechanisms for human oversight and control in AI systems. This learning will focus on the deep analysis of software engineering as it relates to complex understanding of human/AI interactions.

Governance, Risk, and Compliance provides learners with advanced skills and knowledge to authorize and maintain information systems utilizing various risk management frameworks. The course focuses on the strategic and long-term alignment of an organization's information security program to regulatory requirements and organizational policies. Course topics include compliance and regulatory requirements, data classification and prioritization, security and privacy controls, compliance audits and remediation, and risk management plans.