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.

MSAIML CURRICULUM OVERVIEW

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.

Master of Science in Artificial Intelligence and Machine Learning

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