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The MS in Artificial Intelligence degree provides students with the foundational knowledge and technical skills to design and implement artificial intelligence machines and autonomous systems. Students will gain a deep understanding of advanced concepts and techniques in AI, and will learn core relevant areas including machine learning, vision computing, natural language processing, robotics and cognitive science.  The degree prepares students for professional careers as AI/ML engineers or research scientists, managing intelligent systems development and design.

The MS in Artificial Intelligence degree provides students with the foundational knowledge and technical skills to design and implement artificial intelligence machines and autonomous systems. Students will gain a deep understanding of advanced concepts and techniques in AI, and will learn core relevant areas including machine learning, vision computing, natural language processing, robotics and cognitive science.  The degree prepares students for professional careers as AI/ML engineers or research scientists, managing intelligent systems development and design.

Program Requirements Quarter Hours
Introductory Courses 0-24
Degree Requirements 48
Total hours required 48-72

 Learning Outcomes

Students will be able to:

  • Analyze and design architectures for intelligent agents to solve a specific real-world problem.
  • Explain core concepts of advanced machine learning techniques and AI algorithms including, for instance, heuristic search, logical reasoning, supervised and unsupervised learning, deep learning, classification methods, Bayesian networks, natural language processing and image analysis.
  • Identify and combine AI techniques and approaches to solve a specific problem and to develop a modern intelligent application.
  • Use programming skills to implement an AI enabled system in a specific domain.
  • Explain and define categories of ethical dilemmas posed by the coming revolution in intelligent and autonomous computing systems.
  • Work independently or in a small team to design and develop an intelligent computer system.
  • Read and present research papers.
  • Communicate effectively the core concepts of an intelligent computer system to a non-technical audience.

Degree Requirements

Course Requirements

No Introductory Course may be substituted for any other course at any level.

Introductory Courses

Introductory courses may be waived for any of the following conditions:

  • The student has the appropriate course work to satisfy an Introductory Course.
  • The student has appropriate and verified professional experience to satisfy an Introductory Course.
  • If an exam is available, the student passes a Graduate Assessment Examination (GAE) in the Introductory Course area.    
Course Title Quarter Hours
CSC 400DISCRETE STRUCTURES FOR COMPUTER SCIENCE4
CSC 401INTRODUCTION TO PROGRAMMING4
CSC 402DATA STRUCTURES I4
CSC 403DATA STRUCTURES II4
CSC 412TOOLS AND TECHNIQUES FOR COMPUTATIONAL ANALYSIS4
IT 403STATISTICS AND DATA ANALYSIS4

Required Courses

Course Title Quarter Hours
CSC 421APPLIED ALGORITHMS AND STRUCTURES4
CSC 480ARTIFICIAL INTELLIGENCE I4
CSC 484ETHICS IN ARTIFICIAL INTELLIGENCE4
CSC 578NEURAL NETWORKS AND DEEP LEARNING4
CSC 580ARTIFICIAL INTELLIGENCE II4
CSC 583NATURAL LANGUAGE PROCESSING4
CSC 587COGNITIVE SCIENCE4
DSC 540ADVANCED MACHINE LEARNING4

Major Electives

Course Title Quarter Hours
Choose eight (8) credits from the following list:8
CMNS 549
SPECIAL TOPICS IN ORGANIZATIONAL COMMUNICATION
CSC 458
SYMBOLIC PROGRAMMING
CSC 481
INTRODUCTION TO IMAGE PROCESSING
CSC 482
APPLIED IMAGE ANALYSIS
CSC 528
COMPUTER VISION
CSC 555
MINING BIG DATA
CSC 575
INTELLIGENT INFORMATION RETRIEVAL
CSC 577
RECOMMENDER SYSTEMS
CSC 594
TOPICS IN ARTIFICIAL INTELLIGENCE
CSEC 440
INFORMATION SECURITY MANAGEMENT (FORMERLY CNS 440)
CSE 475
INTRODUCTION TO ROBOTICS (FORMERLY CSC 475)
DSC 450
DATABASE PROCESSING FOR LARGE-SCALE ANALYTICS
DSC 478
PROGRAMMING MACHINE LEARNING APPLICATIONS
DSC 480
SOCIAL NETWORK ANALYSIS
SE 488
AI-DRIVEN SOFTWARE DEVELOPMENT
SE 489
MACHINE LEARNING ENGINEERING FOR PRODUCTION (MLOPS)

Open Elective

Course Title Quarter Hours
Student must complete four (4) credits of advisor-approved graduate courses from the School of Computing in the range of 421-699. Students may select from the following subjects: CSC, CSE, CSEC, DSC, ECT, GAM, HIT, IS, IT, NET, SE.4

Capstone Requirement

Course Title Quarter Hours
Choose one course from the following list:4
CSC 675
CAPSTONE IN ARTIFICIAL INTELLIGENCE
CSC 695
MASTER'S RESEARCH
CSC 697
GRADUATE INTERNSHIP
The internship option offers students the opportunity to integrate their academic experience with on-the-job training on an AI-related project. Students must enroll in CSC 697 for 4 credit hours to satisfy the capstone requirement. These are the steps: 1) Secure an internship with focus in AI. 2) International Students must obtain the appropriate practical training form and meet with an advisor in the CDM Academic Center for approval. ISS Forms 3) Login to MyCDM and click the “Internships” link on the left to start the course enrollment process.
A student who is working on a research project and has made an original contribution to their area of study may choose to complete a Master's Thesis. Additional information and requirements for School of Computing students pursuing the thesis option can be found on the SoC Master's Thesis Guideline page.