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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|
|Total hours required||52-76|
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.
No Introductory Course may be substituted for any other course at any level.
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.
|CSC 400||DISCRETE STRUCTURES FOR COMPUTER SCIENCE||4|
|CSC 401||INTRODUCTION TO PROGRAMMING||4|
|CSC 402||DATA STRUCTURES I||4|
|CSC 403||DATA STRUCTURES II||4|
|CSC 412||TOOLS AND TECHNIQUES FOR COMPUTATIONAL ANALYSIS||4|
|IT 403||STATISTICS AND DATA ANALYSIS||4|
|CSC 421||APPLIED ALGORITHMS AND STRUCTURES||4|
|CSC 480||ARTIFICIAL INTELLIGENCE I||4|
|CSC 481||INTRODUCTION TO IMAGE PROCESSING||4|
|CSC 484||ETHICS IN ARTIFICIAL INTELLIGENCE||4|
|CSC 578||NEURAL NETWORKS AND DEEP LEARNING||4|
|CSC 580||ARTIFICIAL INTELLIGENCE II||4|
|CSC 583||NATURAL LANGUAGE PROCESSING||4|
|CSC 587||COGNITIVE SCIENCE||4|
|DSC 540||ADVANCED MACHINE LEARNING||4|
|Choose eight (8) credits from the following list:||8|
|SPECIAL TOPICS IN ORGANIZATIONAL COMMUNICATION|
|INTRODUCTION TO ROBOTICS (FORMERLY CSC 475)|
|APPLIED IMAGE ANALYSIS|
|MINING BIG DATA|
|INTELLIGENT INFORMATION RETRIEVAL|
|TOPICS IN ARTIFICIAL INTELLIGENCE|
|INFORMATION SECURITY MANAGEMENT (FORMERLY CNS 440)|
|DATABASE PROCESSING FOR LARGE-SCALE ANALYTICS|
|SOCIAL NETWORK ANALYSIS|
|PROGRAMMING MACHINE LEARNING APPLICATIONS|
|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|
|CSC 675||CAPSTONE IN ARTIFICIAL INTELLIGENCE||4|
|or CSC 695||MASTER'S RESEARCH|