The Online Master of Science in Computer Science and Artificial Intelligence requires the successful completion of 30 units (10 modules, 3 units each). Most students complete the program in 4 semesters of part-time study, following two short presemester bootcamps. The flexible online structure allows students to balance professional and academic commitments while maintaining steady progress toward degree completion. The students can start the program either in the Fall or in the Spring semesters. The program is project-based and not exam-based.
For Fall 2026 entry, orientation and the bootcamp start on Friday, August 14, 2026, and the fall semester starts on Friday, August 28, 2026.
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Preparatory Bootcamp
(Non-credit, Required for Students to refresh their background)
Computer Science Bootcamp (CX501)
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.
Core Curriculum (30 Credits Total)
Students complete eight modular courses organized into two parallel tracks—Artificial Intelligence (AI) and Systems—with one module from each track taken each semester. In addition to these courses they will also attend two modules (each spanning two semesters) one with AI leaders from industry and another completing a capstone project in order to enhance their project portfolio.
AI Curriculum
1. AI Module 1 (DX 601) – Mathematical Foundations of Data Science
This module focuses on the mathematical foundations of data science with a dual focus on linear algebra and probability. The linear algebra component will cover vectors, matrix, tensor, multiplication, inverse, determinant, trace, and norms (L1, L2, etc.). The probability component will cover random variables, distributions, expectation, marginal/conditional probability, independence, and correlation.
2. AI Module 2 (CX 642) – Applied Machine Learning
Prerequisite: CX 641.
This module provides a rigorous introduction to machine learning concepts, techniques, and algorithms, with an emphasis on applied methods for classification, regression, clustering, dimensionality reduction, deep learning, reinforcement learning, generative models, and advanced neural network topics. The focus is on hands-on experience using Python and targeted libraries such as scikit-learn and PyTorch.
3. AI Module 3 (CX643) – Generative Models
Prerequisite: CX 642.
This module introduces the students to the principles and applications of modern generative models in natural language processing, computer vision, and multimodal AI. Students will explore core architectures such as recurrent neural networks, Transformers, large language models (LLMs), variational autoencoders (VAEs), generative adversarial networks (GANs), and diffusion models. Emphasis is on practical implementation, evaluation, and deployment using industry-standard frameworks and responsible AI practices.
4. AI Module 4 (CX644) – Systems Deployment and Responsible Innovation
Prerequisite: CX 643.
This course prepares professionals to transition AI/ML solutions from prototyping to production at scale. It emphasizes MLOps practices, cloud deployment, data engineering, monitoring, and responsible AI governance. Students will master technical workflows as well as organizational dimensions, such as compliance and business integration, while learning security and privacy techniques to safeguard AI systems.
Systems Curriculum
1. Systems Module 1 (CX 651) – Foundations of Programming and Systems
Prerequisite: Programming proficiency (covered in Bootcamp 2).
This module focuses on the design and implementation of systems-oriented applications that combine object-oriented programming, network communication, concurrency control, and data management.
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.
2. Systems Module 2 (CX 652) – Cloud Computing
Prerequisite: CX 651.
This module provides a rigorous yet application-oriented study of cloud computing, leading to both theoretical depth and hands-on expertise. Students will examine the foundations of virtualization, containerization, and cloud architectures, with emphasis on modern infrastructure design, resource management, and orchestration.
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.
3. Systems Module 3 (CX653) – Scalable Data Analytics Systems
Prerequisite: CX 652.
This module 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.
4. Systems Module 4 (CX654) – GPU Computing for Data and Cloud Applications
Prerequisite: CX 653.
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 will evaluate performance trade-offs and analyze dependencies between GPU and AI workloads.
Two (2) 2-semester long 3-unit modules:
1. AI in Industry Series (CX 698): Semesters 1 and 2
No prerequisite.
This module is a series of industry speakers who bring their experience of data analysis and machine learning to the classroom. (Fall & Spring)
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 will practice interpreting professional challenges, articulating solution pathways, and reflecting on the skills required to implement intelligent systems in production environments.
2. Capstone Project (CX 699): Semesters 3 and 4: Capstone project: Build your own LLM from scratch. (Fall and Spring)
Prerequisite: CX 641, CX 642, CX 651, CX 652.
Spanning two semesters, this course introduces students to modern language modeling through project-driven modules. Students will build toward a full language model pipeline, focusing on Transformer architectures, large-scale text preprocessing, GPU/cloud optimization, fine-tuning, evaluation, and deployment. Students will complete a culminating project suitable for inclusion in a professional portfolio.
