Master of Science in Artificial Intelligence
Machine Learning

Master of Science in Artificial Intelligence
and Machine Learning (MSAIML) Degree Program Overview

Increasing technological advancements have made understanding artificial intelligence (AI) and machine learning (ML) vital skills in many industries. At Fairfax University of America, we offer a Master of Science in Artificial Intelligence and Machine Learning (MSAIML) for students, whether or not they have earned a prior computer science degree program. This program  contributes to our mission of empowering students to achieve professional growth and be transformative leaders in a global society.

Master of Science in Artificial Intelligence and Machine Learning Degree Program Objectives

Students enrolled in our MSAIML program must earn 36 credits to graduate. 12 of these credits are core courses, six are career application courses, and the remaining 18 are AI and ML specializations. The coursework features a combination of theory and practice, examining traditional and cutting-edge concepts in the topic.

The program offers the flexibility for students with different backgrounds in AI and ML to pursue an education. Undergraduate students and professionals working in computer science positions can benefit from this instruction and coursework. To expand their knowledge of AI and ML, students also can obtain additional credentials in related topics. The Office of Micro-Credentials offers the following micro-credentials for students in the MSAIML program:

  • Artificial Intelligence/Machine Learning Engineer (ALMLE)
  • AI Specialist (AISP)
  • AWS Machine Learning Engineer (AWSMLE)
  • Robotic Process Automation Programmer (RPAP)

While this program is open to people with various levels of technological experience, students need basic skills for success in this program. Prerequisite classes may need to be completed before progressing to the core courses. Students should review our graduate program admission requirements and the curriculum overview to ensure they meet the necessary criteria for enrollment.

MSAIML Degree Program Course Work

Through this master’s program at Fairfax University of America, students will hone the essential proficiencies needed to be innovative leaders at numerous organizations. Each course focuses on a specific aspect of AI and ML, but the overall outcomes include:

  • Applying AI and ML algorithms to devise intelligent solutions to real-world challenges and automate AI system and part development
  • Implementing ethical, legal, and social principles regarding technology in AI and ML applications
  • Facilitating effective communication between co-workers and cross-functional teams
  • Creating human behavior models to design human-AI systems and assess their performance
  • Enhancing the efficiency of integrated systems to augment human understanding and productivity


Career Outlook With An Artificial Intelligence and Machine Learning Master’s Degree

Since AI and ML incorporate leading technologies, students in this master’s program develop the capabilities to be competent problem-solvers at businesses in nearly every sector, from finance to healthcare.

The following job titles are some of the most rewarding career paths for students completing the MSAIML program. The average annual salaries as stated by the U.S. Bureau of Labor Statistics (BLS) are included:

  • Computer and Information Research Scientist ($126,830)
  • Software Developer ($105,310 to $110,140)
  • Computer Programmer ($89,190)
  • AI and ML Instructor ($85,540)


Earn Your Master’s of Artificial Intelligence
and Machine Learning Degree

Earn your master’s in artificial intelligence and machine learning from a school committed to student success. At Fairfax University of America, we offer an affordable business-focused education at our institution, providing students the flexibility to pursue advanced training in their fields while managing other life responsibilities.

To learn more about our MSAIML program, request information online. You can also fill out an application today to start classes.


The Master of Science in AI and ML requires completion of 36 credits. Students will take 12 credits of core courses, 6 credits for career application, and 18 credits in AI and ML content area.

Area Number of Courses Credits
Core Courses 4 12
Career Application Courses 2 6
Specialization Courses 6 18
Total 12 36
Program Prerequisites

All new AI and ML program students need certain basic skills to prepare them for success in the AI and ML Program. The AI and ML degree provides a broad understanding of the theory and technology of this field. Students who do not have the required background need to take some or all of the prerequisites before taking the core Courses. Thus, to be successful, students must have a background in the following courses.

Code Course Title Prerequisite Microcredentials Credits
COMP 109 Computer Algorithm and Programming Logic Using Python None 3
COMP 260 Introduction to Operating Systems COMP 109 3
COMP 270 Essentials of Networking COMP 109 3
COMP 329 Data Structures and Algorithm Analysis None 3
COMP 350 Database Concepts None 3
Core Courses (4 Courses – 12 Credits)

These courses provide a breadth of foundational knowledge to implement computer interfaces, software design, communication between systems, and how to manage IT systems. These are all crucial elements for IT professionals to apply these building blocks to any given system or project.

Code Course Title Prerequisite Microcredentials Credits
COMP 501 Advanced Operating Systems COMP 260 3
COMP 502 Design and Analysis of Algorithms COMP 329 3
COMP 503 Networking and Telecommunications COMP 270 3
COMP 504 Database Management Systems COMP 350 3
Application Courses (2 Courses – 6 Credits)

These courses offer an opportunity for students to apply what they have learned throughout the program to a practical project or to a master’s thesis. While the practical project provides for application of knowledge acquired throughout the program and would be represent work that could demonstrate career-readiness to potential employers, the thesis would generally serve to demonstrate a student’s research potential and could be used to demonstrate readiness for doctoral work. Regardless of the option, students will demonstrate basic research knowledge and abilities, which would be used toward completion of either the project or thesis.

Code Course Title Prerequisite Microcredentials Credits
COMP 505 Research Methods None 3
Choose One
COMP 681 AI and ML Capstone Project COMP 505 3
COMP 698 Master Thesis COMP 505 3
Specialization Courses (Any 6 Courses – 18 Credits)

These advanced courses cover the depth of topics related to AI and ML and allow students to develop their knowledge based upon their intended professional trajectories.

Code Course Title Prerequisite Microcredentials Credits
COMP 513 Robotics Design and Programming COMP 329 ALMLE/ RPAP 3
COMP 514 Neural Networks None 3
COMP 515 Pattern Recognition None AISP/ RPAP 3
COMP 516 Deep Learning None ALMLE/ AISP 3
COMP 517 Special Topics in AI None 3
COMP 518 Special Topics in ML None AWSMLE 3
COMP 521 Smart Devices Design and applications None AISP/ RPAP 3
COMP 522 Data Mining COMP 504 AWSMLE 3
COMP 593 Internship I in AI and Machine Learning Completion of core courses and 50% of the program courses 3
COMP 610 Cognitive Computing None ALMLE/ AWSMLE 3
COMP 611 Data Warehousing COMP 504 DWE 3
COMP 613 Game Design COMP 502 RPAP 3
COMP 614 Speech Recognition None AISP/ RPAP 3
COMP 617 AWS Certified Machine Learning None AWS Certificate/ AWSMLE 3
COMP 618 10    Google Machine Learning None Google Certificate 3
COMP 693 Internship II in AI and Machine Learning COMP 593 3

NOTE: Students who wish to take a course that is offered by in another program may petition to do so to their advisor by providing justification for the relevance of the addition as part of their professional trajectory, their intended consulting project, and/or personal interest. A maximum of 2 courses from can be applied from another program.