MS in Artificial Intelligence
Train Like the Job Already Started

A Master’s Degree in AI Designed for Builders

The online Master of Science in Artificial Intelligence from DigiPen Institute of Technology is designed for developers, engineers, technical professionals, and adjacent fields who want to move beyond theory and learn how to build real AI systems.

Through project-based coursework and team-based development, you’ll gain experience working across the full AI lifecycle, from data preparation and model development to evaluation and deployment in real-world environments.

Throughout the program, you will:

Analyze complex problems using computing and data science principles
Design and implement AI-based software systems
Collaborate in multidisciplinary technical teams
Communicate technical concepts to stakeholders
Apply ethical and professional standards in AI development

Graduates will be prepared to contribute to modern AI projects across industries where machine learning, automation, and intelligent systems are increasingly essential.

Program Details

100% online
Complete in five semesters part-time
30 credit hours
Start Terms: Fall, Spring, Summer
Courses: 3 credits each

Build Skills Across the AI Development Lifecycle

The curriculum is designed to help you build capabilities across the full artificial intelligence development process.

Key focus areas include:

Machine learning fundamentals
Neural networks and deep learning
Large language models (LLMs)
Retrieval-augmented generation (RAG systems)
Data analysis and visualization
AI systems development
Prompt engineering and generative AI
Software engineering for AI applications

Develop both theoretical understanding and practical implementation skills while working with modern AI tools and development workflows.

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“The intersection of simulation, AI, and game design is enormous. Those three fields cover high-performance computing, modern tools, and human interaction. And if you have those three things, you can go anywhere.”

Dr. Erik Mohrmann DEAN OF FACULTY

Artificial Intelligence Curriculum

The Master of Science in Artificial Intelligence requires 30 credits.

The curriculum includes:

Two core computer science courses
Six AI specialization courses
Capstone experience

Courses progress through beginner, intermediate, and advanced levels, allowing students to build expertise step-by-step.

Semester 1 Courses

CS 5000 Data Structures and Algorithms

This course introduces concepts in data structures and algorithms, the foundational skills necessary for developing a career in computer science and related fields. Topics covered include abstract data types, static and dynamically allocated data types (arrays, linked lists, stacks, queues), hierarchical data types (trees, graphs), hashing schemes, theory of algorithms, correctness theorem, mathematical induction, and recursion. Additional topics include an introduction to divide-and-conquer, dynamic programming, backtracking, and randomized algorithms. Prerequisite: None

CS 5002 Python Programming for Data Analysis

This course covers Python fundamentals and exploratory data analysis. Topics covered include fundamental Python syntax, object-oriented programming in Python, data frames and array manipulation using NumPy and Pandas, basic concepts of data processing, cleaning, and summarization. Data visualization using Matplotlib, and practical AI modeling using scikit-learn are also covered. Prerequisite: None

Semester 2 Courses

CS 5200 Artificial Intelligence and Machine Learning I

This course covers fundamental concepts in data analysis using AI. Topics include key principles of learning theory, methods for model selection and evaluation, regression analysis, and classification algorithms. This course also covers basic concepts of probability and statistics as they are applied in data analysis. Prerequisite: CS 5000 & CS 5002

CS 5201 Data Visualization

This course introduces students to the study, field, and practical application of data visualization. Data visualization strives to convert data into a visual encoding that allows for better comprehension, memory, and decision-making. The course will examine techniques for creating effective static and interactive visualizations through the use of graphic design, perception, psychology, and cognitive science.

Toward the end of the semester, there will be a strong emphasis on critiquing and creating interactive visualizations of complex systems, such as stock market trading systems and large software projects (static code, software architecture, software evolution, software execution, and data flow).

Several projects will involve programming interactive data visualizations for the web. Prerequisite: CS 5000 & CS 5002

Semester 3 Courses

CS 5210 Neural Networks

This course introduces the theory and applications of neural networks and deep learning. Topics include artificial neural networks, backpropagation, hyperparameter selection, and optimization methods in deep learning, convolutional and recurrent neural networks, deep reinforcement learning, and an introduction to generative models. Additional topics may include other recent advancements in deep learning. Prerequisite: CS 5200

CS 5211 AI-Based Data Analysis

This course deep dives into data analysis using AI. Topics include clustering, data reduction, interpretable ML algorithms such as decision trees and random forests, reinforcement learning, and recommender systems. The course also reinforces the data science life cycle through projects. Prerequisite: CS 5000 & CS 5002

Semester 4 Courses

CS 5220 Large Language Models I

This graduate-level course provides a comprehensive exploration of large language models (LLMs), covering their theoretical foundations, architectural innovations, training methodologies, and real-world applications. Topics covered include understanding of transformer architectures, attention mechanisms, and the scaling laws that govern modern language models. The course emphasizes both theoretical principles and practical implementation, preparing students to contribute to cutting-edge research and industrial applications in natural language processing. Prerequisite: CS 5200

CS 5221 Advanced AI Systems

This course provides a hands-on exploration of modern AI technologies, focusing on the practical applications of prompt engineering, large language models (LLMs), and agentic AI. Students will learn how to craft effective prompts for AI systems, understand the inner workings and capabilities of LLMs, and explore high-level applications, like Azure Machine Learning to enhance AI workflows. Students will gain experience analyzing model behavior, retrieval augmented generation, and incorporating AI agents in their systems. By the end of the course, students will be equipped with the knowledge and technical skills needed to leverage AI systems effectively, preparing them for both industry applications and future advancements in artificial intelligence. Prerequisite: CS 5002

Semester 5 — Capstone Experience

CS 5010 Capstone Project

This course supports mixed-discipline game, or game-adjacent, projects in preproduction, production, or post-production with a focus on the application of discipline-based skills. Project and pipeline management techniques will be applied, including team dynamics, cross-discipline integration, and best practices of the product development cycle in game production. Prerequisite: End of Program

What You’ll Build in This AI Master’s Program

Throughout the Master of Science in Artificial Intelligence program, you will work on projects that simulate real AI development environments. These projects help you build demonstrable technical artifacts that show employers what you can do.

Examples of projects you may complete include:

Machine Learning Models

Develop and evaluate predictive models using real-world datasets. Students explore model training, validation, and optimization techniques across multiple algorithms.

Data Analysis Pipelines

Build end-to-end data processing workflows that clean, analyze, and visualize large datasets to support decision-making.

Neural Network Applications

Design and implement deep learning models for tasks such as pattern recognition, classification, and sequence prediction.

Large Language Model Applications

Work with modern language models and transformer architectures to develop applications in natural language processing, generative AI, and conversational systems.

AI System Prototypes

Develop applied AI systems that integrate machine learning models with software applications, APIs, and deployment environments.

These projects are designed to demonstrate practical AI capabilities across the full development lifecycle, from data preparation and modeling to evaluation and deployment.

AI Tools and Technologies You’ll Work With

Gain experience working with technologies commonly used in modern AI development environments. Examples include:

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Programming & Development

Python
NumPy
Pandas

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Machine Learning Libraries

Scikit-learn
Neural network frameworks used in deep learning workflows

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Data Visualization

Matplotlib
Interactive data visualization techniques

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AI System Development

Large language models (LLMs)
Prompt engineering workflows
Retrieval-augmented generation
AI agents and intelligent systems

You’ll also learn software engineering practices used in AI development, including:

Version control
Debugging and testing
Model evaluation and validation
Collaborative development workflows

The goal is to ensure you can work effectively within real AI engineering environments, not just experimental research settings.

Careers in Artificial Intelligence

Graduates of this artificial intelligence master’s program develop skills applicable to a wide range of technical roles in modern computing and data-driven industries.

Possible career paths include:

Machine learning engineer
AI engineer
Data scientist
NLP engineer
Computer vision engineer
Generative AI developer
Data engineer
Software engineer

With industry experience, graduates may progress into roles such as AI lead engineer, principal data scientist, or AI systems architect.

Learn More

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Designed for Builders Across Technical and Adjacent Fields

This AI master’s program is ideal for:

Software engineers seeking to transition into AI and machine learning roles
Developers and technical creators interested in building intelligent systems and data-driven applications
Career changers with technical foundations who want to move into artificial intelligence and data science roles
Professionals from allied fields — such as ethics, urban planning, and policy — who want a substantive, hands-on understanding of AI and how it is developed and applied

Schedule a call with one of our helpful admissions outreach advisors to determine if this program is right for you. Or, if you’re ready, get started on your application.

Start Your Application

Schedule a Call

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Jul

Priority Deadline

July 17

Fall 2026 Term

Aug

Final Deadline

August 17

Fall 2026 Term

Aug

Next Start

August 31

Fall 2026 Term

Online Technology Degree Programs for Builders and Innovators

Advance your career with online master’s programs designed to help you build real systems, develop practical skills, and create portfolio-ready work.

DigiPen Institute of Technology offers online technology degrees built for developers, engineers, and technical creators who want more than theory. Our graduate programs emphasize applied learning, production-style development, and real-world problem solving.

Whether you’re building AI systems or developing interactive game experiences, you’ll gain hands-on experience and graduate with demonstrable work that supports your next career move.

Build Real Systems. Develop Real Skills.

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