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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.

Program Requirements Quarter Hours
Introductory Courses 0-24
Degree Requirements 52
Total hours required 52-76

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 481INTRODUCTION TO IMAGE PROCESSING4
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
SPECIAL TOPICS IN ORGANIZATIONAL COMMUNICATION
SYMBOLIC PROGRAMMING
INTRODUCTION TO ROBOTICS
APPLIED IMAGE ANALYSIS
COMPUTER VISION
MINING BIG DATA
INTELLIGENT INFORMATION RETRIEVAL
RECOMMENDER SYSTEMS
TOPICS IN ARTIFICIAL INTELLIGENCE
INFORMATION SECURITY MANAGEMENT (FORMERLY CNS 440)
DATABASE PROCESSING FOR LARGE-SCALE ANALYTICS
SOCIAL NETWORK ANALYSIS
PROGRAMMING MACHINE LEARNING APPLICATIONS

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 CPSE, CSEC, DSC, ECT, GAM, HIT, IS, IT, NET, SE. 4

Capstone Requirement

Course Title Quarter Hours
CSC 675CAPSTONE IN ARTIFICIAL INTELLIGENCE4
or CSC 695 MASTER'S RESEARCH