Online Master of Science in Computer Science & AI

The Computer Science Bootcamp prepares incoming students for success by introducing or refreshing core content and tools, including Python programming, local and cloud computing basics, and industry-standard workflows in GitHub. All learners are required to complete the bootcamp two weeks prior to the start of the semester.

This course provides the mathematical foundation for data science and artificial intelligence. It introduces key concepts in linear algebra, probability, and related quantitative methods that support later coursework in machine learning, AI systems, and scalable data applications.

This course provides a rigorous, graduate-level understanding of the core principles underpinning modern computing systems. By integrating hardware architecture, operating systems, memory management, networking, concurrency, and data management, this course equips students with the conceptual and practical tools to analyze, design, and reason about complex software and hardware systems.

This course provides a rigorous introduction to machine learning concepts, techniques, and algorithms. Topics include applied methods for classification, regression, clustering, dimensionality reduction, deep learning, reinforcement learning, generative models, and advanced neural network topics. Students gain hands-on experience using Python and targeted libraries.

Designed for working professionals, this course provides an application-oriented study of cloud computing to build both theoretical depth and practical expertise. Topics include virtualization, containerization, and cloud architectures, with an emphasis on modern infrastructure design, resource management, and orchestration.

The course also covers principles of scalability, distributed computation, and cloud-native systems, teaching students how to design, deploy, and evaluate robust solutions using industry-standard platforms. Security and compliance frameworks are integrated throughout.

This course introduces the principles and applications of modern generative models in natural language processing, computer vision, and multimodal AI. Students explore core architectures, including recurrent neural networks, Transformers, large language models, variational autoencoders, generative adversarial networks, and diffusion models. Emphasis is placed on practical implementation, evaluation, and deployment using industry-standard frameworks and responsible AI practices.

This course provides a comprehensive study of distributed and scalable data analytics systems, combining theoretical foundations with hands-on experience using modern cloud and big-data technologies. Students learn to construct and operate large-scale data processing pipelines, with an emphasis on practical scalability, fault tolerance, and reliability. These skills are directly applicable to professional industry environments.

This course prepares professionals to transition AI and machine learning solutions from prototyping to production at scale. It emphasizes MLOps practices, cloud deployment, data engineering, monitoring, and responsible AI governance.

Students master technical workflows as well as organizational dimensions, such as compliance and business integration, while learning security and privacy techniques to safeguard AI systems.

This course introduces the fundamentals of GPU programming and accelerated computing. It emphasizes conceptual understanding and hands-on evaluation of performance. Topics include the role of GPUs in parallel computing and modern workloads, the development of simple CUDA kernels to understand GPU execution models, and the NVIDIA CUDA ecosystem of libraries and tools.

Students evaluate performance trade-offs and analyze dependencies between GPU and AI workloads.

This two-semester course explores how complex AI and software systems are developed and deployed in real-world environments. Through case studies and conversations with industry practitioners and program alumni, students examine how technical teams approach complex engineering problems, make design decisions, and navigate the constraints of large-scale systems.

The course emphasizes analytical reasoning, systems thinking, and professional problem-solving. Students practice interpreting professional challenges, articulating solution pathways, and reflecting on the skills required to implement intelligent systems in production environments.

Spanning two semesters, this course introduces students to modern language modeling through project-driven modules. Students build toward a full language model pipeline, focusing on Transformer architectures, large-scale text preprocessing, GPU and cloud optimization, fine-tuning, evaluation, and deployment.

Students complete a culminating project suitable for inclusion in a professional portfolio.