MS AI & Machine Learning - Colorado State University Global

Advance Into High-Demand AI & Machine Learning Careers

CSU Global’s Master of Science in Artificial Intelligence and Machine Learning is designed for working professionals with prior technical or quantitative coursework who want applied—not purely theoretical—AI roles. This is not an entry-level degree in programming or analytics. It is built for professionals ready to move into advanced AI roles and emphasizes applied, employer-ready skills that connect advanced theory to real-world problem-solving.

Gain Job-Ready AI Skills Employers Need Now

You’ll build expertise in artificial intelligence, machine learning, algorithms, computer vision, and software development—skills employers are hiring for across high-growth industries. Graduates apply AI and ML techniques to real-world scenarios, design intelligent systems, integrate data-driven solutions, and evaluate performance and outcomes.

Built for Working Professionals—100% Online

Designed to fit real careers and real schedules:

Program format: 30 credits, 100% online
Course structure: Accelerated 8-week courses with multiple start dates
Admissions: No GRE required. Simple online application. Start in as little as one month.
Cost clarity: $675 per credit. No out-of-state tuition. No fees.
Time to completion: About 20 months, taking one course at a time
Flexibility: Finish sooner with transfer credits or a higher course load
Student support: Structured pacing, consistent deadlines, and faculty support

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Get a degree that pays off—literally

Tuition you can count on

CSU Global offers a tuition guarantee that is second to none. Once enrolled at CSU Global, we guarantee your tuition rate will remain the same from your first class to your last.

An investment you can afford

To make things even more affordable, more than 95% of students receive financial aid or employer tuition benefits.

Earn a master’s degree that could pay for itself in under a year

Graduates have reported salary increases of up to 33% after completing the program.

Total program cost (no transfer credits): $20,250

Median reported salary increase:

$25,500 within 1-2 years
$39,000 within 3-5 years

Career paths supported by this degree

This program prepares graduates for advanced AI roles, including:

Machine Learning Engineer – Median salary $160,000¹

AI Engineer – Median salary $155,000¹

Data Scientist – Median salary $112,590²

AI Solutions Architect – Median salary $170,000¹

This graduate course provides a detailed overview of fundamental programming, design and testing concepts. Students are introduced to programming constructs and learn how to plan and create basic programming applications. Students will develop applications using common programming structures, which include: conditional statements, switches, loops, iteration control structures, and arrays.

Credits: 3

In this course, students will be prepared to analyze organizational issues surrounding programming, network and software development, and propose the necessary solutions to address business needs. Students gain a detailed understanding of how to manage, oversee, plan and maintain technical personnel and resources. Students will also learn how to effectively relay technical information to all stakeholders in an organization. Topics include management principles for programmers and development specialists, technical communications, organizational leadership, risk management, project management, and systems maintenance. Students will prepare for cross-functional positions in team and industry settings in this course.

Credits: 3

In this course, students will learn about the ethical considerations and issues programmers and software developers encounter in the workplace related to data, electronic communication, and information security. Students will evaluate and interpret current policies and regulations and formulate their own policies based on these models. Students will also discuss the implications for ethical decisions by technical professionals and leadership utilizing Case Studies and Problem-based learning. Students will learn best practices in ethical decision-making for the work place in this course. Prerequisite: CSC501

Credits: 3

This graduate course provides students with a foundational knowledge in the design and analysis of algorithms. Students will make use of appropriate data structures. Complexity and analysis of algorithms will be completed focusing on worst case and average case, lower bounds, NP-completeness, and recurrences. Students will explore the complexity of appropriate searching, sorting, and graphing algorithms. Prerequisite: CSC505 Principles of Software Development

Credits: 3

This graduate course provides students with an understanding of principles associated with Artificial Intelligence (AI). Students will determine how to utilize structures to represent graphs associated in data exploration. Students will gain an understanding of how to efficiently apply knowledge representation and techniques associated with AI reasoning. Topics that students will explore include techniques efficiently applying game theory, integer programming, continuous optimization, and probability analysis.

Credits: 3

This Graduate course provide students with foundational knowledge in the areas digital image construction and processing. Students will explore topics associated with image formation, image acquisition, and image geometry. The course will expose students to the techniques required to efficiently analyze images for representation in applicable context scenarios. Students will apply image processing techniques for filtering and edge detection for image deconstruction.

Credits: 3

This Graduate course provides students with an understanding of foundations concepts and theories in machine learning. Students will explore foundational topics that include: supervised and unsupervised learning, learning theory, reinforcement learning and adaptive control. Students will gain an understanding of applications of machine learning in areas of data mining, human computer interaction, natural language processing and computer vision. Prerequisite: CSC510 Artificial Intelligence

Credits: 3

This Graduate course provides students with an overview of appropriate theories and models that are used to represent neural networks. Students will gain foundational knowledge in developing constructs to evaluate and represent components associated with neural networks and learning algorithms. Topics for this course include propagation, feedforward networks, perceptrons, and self-organizing networks. Prerequisite: CSC510.

Credits: 3

Prerequisites

Students applying for this degree must have taken an advanced course in Discrete Mathematics, and an advanced course in Probability and Statistics. Students who do not meet this requirement can seek admission with the program chair approval. Students admitted with approval from the program chair who did not meet the requirements will be required to take one or both of the following CSU Global pre-requisite course work or equivalent:

MTH350 – Discrete Mathematics
MIS446 – Statistics for Data Analytics Using R

To meet pre-requisite requirements for admission, students must complete the above coursework within 12 months of starting and maintain a 3.0 grade point average. Students should reference the provisional admissions policy in the university catalog for additional admission requirements.

Note: Some Master of Science in Artificial Intelligence and Machine Learning students may also be required to take RES500 – Fundamentals of Quantitative Analysis as part of their program if they have an undergraduate GPA below 3.00. In this case, the degree is 33 credits.

Earn a Master’s in Artificial Intelligence & Machine Learning Online

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Advance Into High-Demand AI & Machine Learning Careers

Gain Job-Ready AI Skills Employers Need Now

Built for Working Professionals—100% Online

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Get a degree that pays off—literally

Tuition you can count on

An investment you can afford

Earn a master’s degree that could pay for itself in under a year

Career paths supported by this degree

¹ Salary estimates based on aggregated job market data (e.g., Glassdoor, LinkedIn, industry reports).

² U.S. Bureau of Labor Statistics, May 2024 median pay. Roles such as Machine Learning Engineer and AI Engineer are commonly classified under Software Developers or Computer & Information Research Scientists by the U.S. Bureau of Labor Statistics.

Artificial Intelligence and Machine Learning Courses

CSC500 - Principles of Programming

CSC501 - Management for the Computer Science Professional

CSC502 - Ethical Leadership in Software Development

CSC505 - Principles of Software Development

CSC506 - Design and Analysis of Algorithms

CSC507 - Foundations of Operating Systems

CSC510 - Foundations of Artificial Intelligence

CSC515 - Foundations of Computer Vision

CSC525 - Principles of Machine Learning

CSC580 - Capstone: Applying Machine Learning and Neural Networks

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