MS in Artificial Intelligence
Artificial Intelligence (AI) technologies are transforming sectors such as healthcare, finance, transportation, and entertainment, driving the need for highly skilled professionals who can plan, implement, and manage AI solutions.
The MS in AI provides students with the theoretical knowledge and practical skills necessary to tackle complex problems, innovate in cutting-edge technologies, and remain competitive in a job market increasingly dominated by AI applications.
Why get a master’s in AI at the University of Delaware?
The need for highly skilled technical AI experts goes beyond the ability to use existing software libraries and apply out-of-the-box solutions. These advanced professionals are crucial for pushing the boundaries of AI research and development, creating custom algorithms, and optimizing AI models for specific applications. The MS in AI program gives students a deep understanding of the underlying principles of machine learning, neural networks, optimization, algorithm design and analysis.
In areas such as natural language processing, computer vision, and networks, AI experts must innovate and solve problems that pre-packaged solutions broadly used today cannot address. Such experts are essential for advancing AI capabilities, ensuring systems are more efficient and accurate, and capable of handling complex, real-world challenges. In our MS program in AI, we train these high-level professionals who will lead the future of AI innovation and drive progress in various fields, from developing new AI methodologies to creating sophisticated solutions tailored to national needs.
Applications are open until
August 19, 2025.
Key Numbers
2 Years
per credit hour (2024-25)
30
Credit Hours
Admission Requirements
The following are the general requirements for admission to our graduate program:
- The equivalent of a bachelor’s degree at the University of Delaware. A minimum grade average of 3.2 in the major field of study and an overall cumulative index of 3.0 is required.
- Scholarly competence in mathematics and computer programming. Applicants are expected to know the material covered by at least one undergraduate course in each of the following topics: structured high-level language programming; data structures; computer architecture; operating systems; and analysis of algorithms.
- Applicants must have completed the equivalent of at least four undergraduate courses in the following topics: calculus; discrete mathematics; probability and statistics; mathematical logic; and comparable formal subjects, such as Theory of Computation.
- Applicants must have completed at least one undergraduate course in such areas as machine learning, data mining, AI, or data science.
- Minimum GRE scores of 153 for the verbal section, 155 for the quantitative section, and 4.0 for the analytical writing section. The GRE subject test is not required.
- A satisfactory level of proficiency in the English language. For international applicants, The University requires an official TOEFL score of at least 79 on the Internet-based test or IELTS test score of 6.5 overall with no individual speaking score below 6.0. A TOEFL score of at least 100 or IELTS score of 7.0 overall is required to be considered for a teaching assistantship.
- Three (3) letters of recommendation from professors (preferably), employers, or others who are able to assess your potential for success in graduate studies.
- Graduate Application Essay.
- Curriculum vitae.
Admission to the graduate program is competitive. Those who meet stated minimum requirements are not guaranteed admission, nor are those who fail to meet all of those requirements necessarily precluded from admission if they offer other appropriate strengths.
Degree Requirements
The MS in Artificial Intelligence requires a total of 30 credits.
The non-thesis track is intended for students who view the MS as a terminal professional degree. The thesis track is intended for students who are also interested in research in computer and information sciences and may consider pursuing a PhD.
Students begin the program following the non-thesis track. Admission does not guarantee that a student can follow the thesis track. Entry to the thesis track occurs once the student’s advisory committee is approved by the Department.
Non-thesis Track Requirements
The coursework requirement comprises five categories. The Fundamentals of AI and Computing (Category 1) ensures that all students are introduced to the necessary technical background for AI and the subsequent categories. The Topics in Mathematics and Theoretical Computer Science Closely Related to AI (Category 2) ensures a deeper understanding of the mathematical methods underlying AI and machine learning algorithms. The Topics in AI (Category 3) contains fundamental technical courses focused on one or more subfields of AI that are generally applicable in many different domains. The Electives (Category 4) 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. The Seminar (Category 5) 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.
Thesis Track Requirements
The coursework requirement comprises five categories. The Fundamentals of AI and Computing (Category 1) ensures that all students are introduced to the necessary technical background for AI and the subsequent categories. The Topics in Mathematics and Theoretical Computer Science Closely Related to AI (Category 2) ensures a deeper understanding of the mathematical methods underlying AI and machine learning algorithms. The Topics in AI (Category 3) contains fundamental technical courses focused on one or more subfields of AI that are generally applicable in many different domains. The Research (Category 4) for the thesis option, all six credits must be taken as research credits under the supervision of one of the CIS faculty. The Seminar (Category 5) 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.
The MS in AI requires a total of 30 credits with the following constraints:
- Twelve (12) credits must be taken in Category 1.
- Three (3) credits must be taken in Category 2.
- Nine (9) credits must be taken in Category 3. At least three (3) of them must be at the 8xx level.
- Six (6) credits must be taken in Category 4. For the thesis option, all six credits must be taken as research credits under the supervision of one of the CIS faculty.
- Each semester, all graduate students must be enrolled in CISC890 Departmental seminar to satisfy the requirement of Category 5.
- All courses in Categories 1-4 must be taken using the standard grading option (i.e., letter grades).
Courses in Category 2 may satisfy Categories 3 and 4. Courses in Category 3 may satisfy Category 4. However, the same course may not be used to satisfy more than one Category.
Category 1: Fundamentals of AI and Computing (12 credits)
- CISC 684 Introduction to Machine Learning
- CISC 681 Artificial Intelligence
- CISC 621 Algorithm Design and Analysis
- CISC 889 Ethics in AI
Category 2: Topics in mathematics and theoretical computer science closely related to AI and machine learning (3 credits)
- MATH 612 Computational Methods for Equation Solving and Function Minimization
- MATH 631 Introduction to Stochastic Processes
- MATH 630 Probability Theory and Applications
- STAT 603 Statistical Computing and Optimization
- STAT 617 Multivariate Methods and Statistical Learning
- CISC 604 Logic in Computer Science
Category 3: Topics in AI (9 credits)
- CISC 642 Introduction to Computer Vision (or ELEG 604 Imaging and Deep Learning)
- CISC 686 Introduction to Multi-Agent Systems
- CISC 688 Introduction to Natural Language Processing
- CISC 689 Topics in AI
- CISC 683 Introduction to Data Mining
- CISC 822 Graph Mining (or STAT 622 Statistical Network Analysis)
- CISC 817 Large Scale Machine Learning (or cross-listed ELEG 817)
- CISC 820 Quantum Algorithms
- CISC 886 Multi-Agent Systems
- STAT 619 Time-Series Analysis
- ELEG 602 Advanced Machine Learning
- CISC 889 Advanced Topics in AI
- Other fundamental technical courses related to generally applicable in different domains AI that we could approve in the future. Requires formal consent of the adviser or program director.
Category 4: Electives (6 credits)
- QSEG 851 Advanced Topics in Quantum Information
- BINF 620 Big Data Analytics in Healthcare
- BINF 644 Bioinformatics
- BINF 695 Computational Systems Biology (or cross-listed ELEG 697)
- CISC 844 Computational Biomedicine (or cross-listed BMEG 844)
- CISC 682 Introduction to Human-Computer Interaction
- CISC 685 Modeling and Simulation for Bioinformatics Systems
- CISC 601 Elements of the Theory of Computation
- CISC 848 Computing and Data Science for Soft Materials Innovation (or cross-listed MSEG 848, ELEG 848)
- MEEG 671 Introduction to Robotics
- CISC 649 Introduction to Autonomous Driving
- CPEG 657 Search and Data Mining
- ELEG 601 Optimization for signal processing, machine learning and data science
- ELEG 630 Information Theory
- CISC 830 Combinatorial Optimization and Advanced Algorithms
- LING 604 Structure of Language
- CGSC 696 Psycholinguistics
- CGSC 670 Elements of Cognitive Science
- AINT 699 Industry or academic internship for up to 3 See Section Internships for the details.
- AINT 869 Master’s Thesis in AI (6 credits)
Courses CISC889 (Advanced Topics in AI) and CISC689 (Topics in AI) can be taken multiple times as they offer different sections, each corresponding to different subjects within AI. The same subjects cannot be taken under both CISC889 and CISC689. Students with questions or doubts about the similarity between subjects offered across these courses should consult with the program director or adviser to ensure the course selections align properly with the academic goals and program requirements.
Deadlines
Fall Admission:
February 1: Priority consideration for admission
August 19, 2025: Final deadline to apply (for 2025-26 academic year)
Spring Admission:
December 1: Final deadline to apply
Graduate Recruitment Contacts
Computer and Information Sciences Department
Email: cis-gradprgm@udel.edu
UD Graduate Admissions
Email: gradadmissions@udel.edu
