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.
Request Information
Preparatory Bootcamps
(Non-credit, Required for Students to refresh their background)
Bootcamp 1: ML Preparation Bootcamp
Refresh Python skills (Install Anaconda, Jupyter, and Python libraries (NumPy, pandas, matplotlib, scikit-learn), revisit basic skills in working with data using Pandas. Revisit some core math concepts such as linear algebra, probability and optimization. Understand an AI/ML pipeline at a high level (from data, to training, to evaluation, to prediction).
Bootcamp 2: Programming and System Preparation Bootcamp
Introduction to python programming, notebooks, data structures in python, basic python libraries, some small-scale programming project, git basics (clone, commit, push), debugging, testing, introduction to processes, threads and servers, run multiple programs at once, make a simple HTTP request with Python, introduction to Docker, to “the cloud”, learn how to package a script in a Docker container, run it locally.
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 (CX 641) – Introduction to AI and ML
Prerequisite: Programming and ML proficiency (covered in Bootcamp 1&2).
In this module, the students will focus on (a) Probability and Statistics Foundations, (b) Practical Applications of linear algebra, and (c) Modeling and Evaluation of ML models.
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 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.
4. AI Module 4 (CX644) – Systems Deployment and Responsible Innovation
Prerequisite: CX 643.
This module prepares working professionals to take AI/ML solutions from research and prototyping into production at scale. It emphasizes MLOps practices, cloud deployment, data engineering, monitoring, and responsible AI governance.
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.
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.
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, using distributed storage systems and machine learning serving frameworks. The module emphasizes practical scalability, fault tolerance, and reliability.
4. Systems Module 4 (CX654) – GPU Computing for Data and Cloud Applications
Prerequisite: CX 653.
This module introduces the fundamentals of GPU programming and accelerated computing. Rather than diving deep into low-level hardware optimization, it emphasizes conceptual understanding and hands-on evaluation of performance
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)
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.
This module spans two semesters and introduces the students to modern language modeling through hands-on, project-driven modules. Each week, students complete a practical module building progressively toward a full language model pipeline. The course emphasizes transformer architectures, large-scale text preprocessing, GPU/cloud optimization, fine-tuning, evaluation, and deployment.
