Master of Science in Artificial Intelligence
Machine Learning


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In support of the university’s mission, the Master of Science in Artificial Intelligence and Machine Learning (MSAIML) is designed to appeal to a broad range of individuals. The program balances theory with practice, offers an extensive set of traditional and state-of-the-art courses, and provides the necessary flexibility to accommodate students with various backgrounds, including computer professionals who want to expand their understanding of AI & ML, as well as individuals whose undergraduate degrees are not in Computer Science but wish to broaden their knowledge in AI & ML.


  • Apply AI and ML algorithms to draw inferences, design smart application to solve real-world problems, and to automate the development of AI systems and components.
  • Model human behaviors to develop Human-AI systems and evaluate their performance.
  • Improve overall integrated system performance to influence human performance and learning.
  • Apply social, ethical, and legal principles of technologies and their applications in the AI and ML field.
  • Communicate effectively individually or in cross-functional teams.


  • AI Specialist
  • Applied Artificial Intelligence and Machine Learning-Scientist
  • AWS Machine Learning Engineer
  • Robotic Process Automation Programmer
  • Artificial Intelligence Engineer
  • Robotics Programmer
  • Machine Learning Engineer
  • Instructor at a college or university teaching AI and ML in addition to Computer Science courses


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


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.


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