Artificial Intelligence (MS)

The MS in Artificial Intelligence program aims to equip students with the advanced knowledge and technical skills required to address complex problems in the field of AI. The program's thesis and non-thesis tracks provide tailored pathways for students pursuing careers in academia, industry, or entrepreneurship producing leaders in AI who can drive technological advancements and address the challenges of an AI-driven world. Graduates will:

Utilize and develop cutting-edge AI technologies, such as machine learning, neural networks, natural language processing, and computer vision, to design innovative solutions tailored to real-world challenges.
Demonstrate a rigorous understanding of theoretical foundations and practical applications to excel in research, development, and implementation of AI systems
Ethically and responsibly analyze and address the societal impacts of AI technologies
Demonstrate competencies in critical thinking and problem-solving in AI.

Program Policy Document:

Please see the Program Policy Document for more information.

Fundamentals of AI and Computing

Category 1: The Fundamentals of AI and Computing ensures that all students are introduced to the necessary technical background for AI and the subsequent categories. Twelve (12) credits must be taken in this category.

CISC 621 - Algorithm Design and Analysis Credit(s): 3
CISC 681 - Artificial Intelligence Credit(s): 3
CISC 684 - Introduction to Machine Learning Credit(s): 3
CISC 889 - Advanced Topics in Artificial Intelligence Credit(s): 3

Topics in mathematics and theoretical computer science closely related to AI and machine learning

Category 2: The Topics in Mathematics and Theoretical Computer Science Closely Related to AI and machine learning ensures a deeper understanding of the mathematical methods underlying AI and machine learning algorithms. Three credits must be taken in this category.

CISC 604 - Logic in Computer Science Credit(s): 3
MATH 612 - Computational Methods for Equation Solving and Function Min Credit(s): 3
MATH 630 - Probability Theory and Applications Credit(s): 3
MATH 631 - Introduction to Stochastic Processes Credit(s): 3
STAT 603 - Statistical Computing and Optimization Credit(s): 3
STAT 617 - Multivariate Methods and Statistical Learning Credit(s): 3

Topics in AI

Category 3: The Topics in AI contains fundamental technical courses focused on one or more subfields of AI that are generally applicable in many different domains. Nine credits must be taken in this category. Can include courses from Category 2.

CISC 642 - Introduction to Computer Vision Credit(s): 3
CISC 683 - Introduction to Data Mining Credit(s): 3
CISC 686 - Introduction to Multi-Agent Systems Credit(s): 3
CISC 688 - Introduction to Natural Language Processing Credit(s): 3
CISC 689 - Topics: Artificial Intelligence Credit(s): 3-12
CISC 822 - Graph Mining Credit(s): 3
CISC 889 - Advanced Topics in Artificial Intelligence Credit(s): 3
ELEG 602 - Deep Learning and Generative Models Credit(s): 3
ELEG 604 - Imaging and Deep Learning Credit(s): 3
ELEG 817 - Large Scale Machine Learning Credit(s): 3
QSEG 820 - Quantum Algorithms Credit(s): 3
STAT 619 - Time Series Analysis Credit(s): 3
STAT 622 - Statistical Network Analysis Credit(s): 3

Electives/Thesis

Category 4: The Electives are either fundamental technical CS courses generally applicable in many different domains of AI or application/domain specific courses in which AI plays a crucial role. Can include courses from Categories 2 and 3. The total of 6 credits can be taken in Category 4 (Electives). Alternatively, the student can take MS Thesis in AI for 6 credits.

AINT 699 - Internship in Artificial Intelligence Credit(s): 1-3
AINT 869 - Master’s Thesis in Artificial Intelligence Credit(s): 1-6
BINF 620 - Big Data Analytics in Healthcare Credit(s): 3
BINF 644 - Bioinformatics Credit(s): 3
BINF 685 - Modeling and Simulation for Bioinformatics Systems Credit(s): 3
BINF 695 - Computational System Biology Credit(s): 3
CGSC 670 - Elements of Cognitive Science Credit(s): 3-6
CGSC 696 - Psycholinguistics Credit(s): 3
CISC 601 - Elements of the Theory of Computation Credit(s): 3
CISC 649 - Topics in Computer Applications Credit(s): 3
CISC 682 - Introduction to Human-Computer Interaction Credit(s): 3
CISC 830 - Combinatorial Optimization and Advanced Algorithms Credit(s): 3
CISC 844 - Computational Biomedicine Credit(s): 3
CPEG 657 - Search and Data Mining Credit(s): 3
ELEG 601 - Optimization for Signal Processing, Machine Learning and Data Science Credit(s): 3
ELEG 630 - Information Theory Credit(s): 3
LING 604 - Structure of Language Credit(s): 3
MEEG 671 - Introduction to Robotics Credit(s): 3
QSEG 851 - Advanced Topics in Quantum Information Credit(s): 3

Seminar

Category 5: The Seminar prepares students to successfully complete the MS and exposes them to broader issues such as professional and ethical responsibilities and the impacts of computer and information sciences on society. Must be taken every fall and spring semester.

CISC 890 - Colloquium: Computer Science Credit(s): 0