MS in Artificial Intelligence
About the Program
The Artificial Intelligence master's degree program is designed as a 30-credit hour curriculum that give students a comprehensive framework for artificial intelligence with one of 4 concentration areas: (1) Computer Vision, (2) Intelligent Interaction, (3) Machine Learning, and (4) Knowledge Management and Reasoning.
Students will engage in an extensive core intended curriculum to develop depth in all the core concepts that build a foundation for artificial intelligence theory and practice. Also, they will be given the opportunity to build on the core knowledge of AI by taking a variety of elective courses selected from colleges throughout campus to explore key contextual areas or more complex technical AI applications.
The program will be accessible to both full-time and part-time students, aiming to train students who aspire to have AI research and development (R&D) or leadership careers in industry. To accommodate the needs of working professionals who might be interested in this degree program, the course offerings for the MS in AI will be in the late afternoon and evening hours to allow students to earn the degree through part-time study. The program may be completed entirely on campus, entirely online, or through a combination of on-campus and online courses.
For general program information and questions please contact the Program Coordinator: Kimberly LaPere ([email protected]). For specific curriculum questions, please contact the program director: Dr. Mohamed Abouelenien ([email protected]). If you have questions about the application process, deadlines, tuition, visas, scholarships and assistantships, and other questions please contact the Office of Graduate Studies ([email protected]).
Program Details
Learning Goals
1: Understand representations, algorithms and techniques used across works in artificial intelligence and be able to apply and evaluate them in applications as well as develop their own.
2: Understand and apply machine-learning techniques, in particular to draw inferences from data and help automate the development of AI systems and components.
3: Understand the various ways and reasons humans are integrated into mixed human-AI environments, whether it is to improve overall integrated system performance, improve AI performance or influence human performance and learning.
4: Understand the ethical concerns in developing responsible AI technologies.
5:Implement AI systems, model human behavior, and evaluate their performance.
Regular admission to the program requires a bachelor’s degree in a science, technology, engineering, or mathematics (STEM) field earned with an average of B (or better) from an accredited program. An entering student should have completed the relevant courses in programming, mathematics, and statistics (see the list below). A course in calculus III and a course in linear algebra are recommended but not required.
Programming
- CIS 200/CIS 2001 (or equivalent) required
- CIS 350 (or equivalent) required
Mathematics
- MATH 116 (or equivalent) required
- CIS 275 or ECE/Math 276 (or equivalent) required
- MATH 227 (or equivalent) recommended
Statistics (only one of these or equivalent required)
- IMSE 317
- STAT 326
- MATH 425
Deficiencies in the prerequisites may be made up after entrance into the program. Students with such deficiencies must complete the missing prerequisite course(s) with a grade of “B” or better within the first two semesters after entering the program.
International applicants, applicants whose native language is not English or who have received their bachelor's or master's degree from outside the United States, Australia or England please refer to the following English Proficiency requirements
Programming
CIS 200 or CIS 2001 Computer Science II
- Prerequisites:MATH 115 and (CIS 150 or IMSE 150 or CCM 150)
- Description: This course presents techniques for the design, writing, testing, and debugging of medium-sized programs, and an introduction to data structures (stacks, queues, linked tests) using an object-oriented programming language. Topics covered include pointers, templates, and inheritance. The principles of UML modeling are continued. This course will consist of three lecture hours and one two-hour laboratory. (4 credits).
CIS CIS 350 - Data Structures and Algorithm Analysis
- Prerequisites: MATH 115 and (CIS 200 or IMSE 200) and CIS 275
- Description: A focus on data and algorithm design. Data design topics include object-oriented discussions of hashing, advanced tree structures, graphs, and sets. Algorithm design topics include the greedy, divide-and- conquer, dynamic programming, backtracking, and branch-and-bound techniques. A significant discussion of algorithm complexity theory, including time and space trade-offs and elementary computability theory, is included. (4 credits ).
Mathematics
MATH 116 Calculus II
- Prerequisites:MATH 115
- Description:Transcendental functions, techniques of integration, improper integral, infinite sequences and series, Taylor's theorem, topics in analytic geometry, polar coordinates, and parametric equations. This course includes computer labs. (4 credits).
CIS 275 or ECE/Math 276 Discrete Structures I
- Prerequisites:MATH 115 and (CIS 200 or CIS 2001)
- Description:An introduction to various topics in discrete mathematics, such as set theory, mathematical logic, functions, counting, advanced counting techniques, relations, trees, graphs, and Boolean algebra. Applications to relational databases, modeling reactive systems, and program verification are also discussed. (4 credits).
MATH 227 Introduction to Linear Algebra (Recommended)
- Prerequisites:MATH 116
- Description:An introduction to the theory and methods of linear algebra with matrices. Topics include: systems of linear equations, algebra of matrices, matrix factorizations, vector spaces, linear transformations, eigenvalues and eigenvectors, science and engineering applications, and computational methods. (3 credits).
Statistics
(one of these courses or equivalent required)
IMSE 317 Engineering Probability and Statistics
- Prerequisites:MATH 116 or MATH 114
- Description: Set theory, combinatorial analysis, probability and axioms, random variables, continuous and discrete distribution functions, expectations, Chebychev's inequality, weak law of large numbers, central limit theorem, sampling statistics and distributions, point and interval estimation and linear regression. (3 credits).
STAT 326 Applied Statistics II
- Prerequisites:STAT 325
- Description: A continuation of STAT 325. This course treats both the principles and applications of statistics. Elementary theory of estimation and hypothesis testing, the use of the normal, chi- square, F and t distributions in statistics problems will be covered. Other topics selected from regression and correlation, the design of experiments, analysis of variance, analysis of categorized data, nonparametric inference, and sample surveys. (3 credits).
MATH 425 Mathematical Statistics
- Prerequisites:MATH 325
- Description: Interval estimation and pivotal quantities, maximum likelihood estimation, hypothesis tests, linear models and analysis of variance, bivariate normal distribution, regression and correlation analysis, and nonparametric methods. (3 credits).
Curriculum
To satisfy the requirements for the MS degree in Artificial Intelligence, all students admitted to the program are expected to complete thirty semester hours of graduate coursework, with a cumulative grade point average of B or better. The program of study consists of core courses, concentration courses, and electives with coursework/ project/thesis option.
Please contact the Computer and Information Science Department about the policy on the minimum grade for a course to satisfy graduation requirements.
The 30 semester hours of required coursework are distributed as follows:
AI foundation (12 credits)
- CIS 579 Artificial Intelligence
- CIS 581 Computational Learning OR ECE 579 Intelligent Systems
- CIS 505 Algorithm Design and Analysis
- CIS 553 Software Engineering
3 Concentration Courses drawn from one of the concentrations (9 Credits)
Concentration 1: Computer Vision
- CIS 515 Computer Graphics
- CIS 551 Advanced Computer Graphics
- CIS 552 Information Visualization and Virtualization
- CIS 652 Advanced Information Visualization and Virtualization
- ECE 577 Autonomous Unmanned Aerial Systems
- ECE 585 Pattern Recognition
- ECE 586 Digital Image Processing
- ECE 587 Selected Topics in Computer Vision
- ECE 588 Robot Vision
- ECE 5831 Pattern Recognition and Neural Networks
- HCDE 530: Information Visualization
Concentration 2: Intelligent Interaction
- CIS 582 Trustworthy Artificial Intelligence
- CIS 585 Advanced Artificial Intelligence
- CIS 587 Computer Game Design and Implementation I
- CIS 588 Computer Game Design and Implementation II
- CIS 679 Research Advances in Computational Game Theory and Economics
- ECE 531 Intelligent Vehicle Systems
- ECE 545 Introduction to Robotic Systems
- ECE 544 Mobile Robots
- IMSE 548: Research Methods in Human Factors and Ergonomics
- IMSE 577 Human-Computer Interaction for UI and UX Design
Concentration 3: Machine Learning
- CIS 511 Natural Language Processing
- CIS 536 Text Mining and Information Retrieval
- CIS 581 Computational Learning
- CIS 583 Deep Learning
- CIS 585 Advanced Artificial Intelligence
- ECE 552 Fuzzy Systems
- ECE 555 Stochastic Processes
- ECE 579 Intelligent Systems
- ECE 583 Artificial Neural Networks
- ECE 679 Advanced Intelligent Systems
- IMSE 505 Applied Optimization
- IMSE 606 Advanced Stochastic Processes
Concentration 4: Knowledge Management and Reasoning
- CIS 511 Natural Language Processing
- CIS 536 Text Mining and Information Retrieval
- CIS 540 Foundation of Information Security
- CIS 552 Information Visualization and Virtualization
- CIS 555 Decision Support and Expert Systems
- CIS 568/ECE 537 Data Mining
- CIS 581 Computational Learning
- CIS 582 Trustworthy Artificial Intelligence
- CIS 583 Deep Learning
- CIS 585 Advanced Artificial Intelligence
- CIS 586 Advanced Data Management
- CIS 685 Research Advances in Artificial Intelligence
- CIS 5700 Advanced Data Mining
- ECE 5001 Analytic and Computational Math
- IMSE 510 Probability & Statistical Modeling
- IMSE 514 Multivariate Statistics
Any course(s) from an MS in AI concentration area(s) outside the student’s concentration can be an elective course(s). Additionally, the elective course(s) can be drawn from other CECS and partner college courses by faculty advisor or program director approval.
Students must choose one of three options: Coursework, MS project, or MS thesis.
This option requires three elective courses from an MS in AI concentration area(s) outside the student’s selected concentration. The minimum requirements for this option are as follows:
Foundation courses – 12 credit hours
Concentration courses – 9 credit hours
Elective courses — 9 credit hours
This option requires a project report describing the results of an independent study project under the supervision of the advisor. The minimum requirements for this option are as follows:
- Foundation courses – 12 credit hours
- Concentration courses – 9 credit hours
- Elective courses — 6 credit hours
- Master’s project – 3 credit hours
This option requires a research thesis prepared under the supervision of the advisor. The minimum requirements for this option are as follows:
- Foundation courses – 12 credit hours
- Concentration courses – 9 credit hours
- Elective courses – 3 credit hours
- Master’s Thesis — 6 credit hours