Campus: NYC
During this 30-credit program, students develop a strong foundation in the theoretical principles and practical techniques that drive modern artificial intelligence innovation. The program is designed to prepare graduates not only to apply existing AI methods but also to develop new approaches that advance the field. Through close engagement with faculty and hands-on learning experiences, students build the expertise needed to solve complex, real-world problems using intelligent systems.
Reflecting the interdisciplinary nature of artificial intelligence, the curriculum integrates perspectives from computer science, psychology, information systems, and other related disciplines offered at Pace. The program is intended for students with strong programming and mathematical backgrounds, typically holding degrees in computing or closely related fields. Students from other academic backgrounds may enter the program after completing bridge coursework to ensure readiness for advanced study.
As AI continues to evolve rapidly, the program’s structure allows students to adapt to emerging technologies and industry demands. Courses are offered on campus, providing an immersive learning environment with direct access to faculty mentorship and university resources.
The program is designed for students with strong programming and mathematical skills with a degree in computing or relevant area. Students with a degree in another area will be able to enroll in the program after having taken up to 3 bridge courses. The bridge courses do NOT count toward the degree; grades earned however are computed into the student's QPA.
| Code | Title | Credits |
|---|---|---|
| CS 608 | Algorithms and Computing Theory | 3 |
| CS 660 | Mathematical Foundations of Analytics | 3 |
| CS 661 | Python Programming | 3 |
| Code | Title | Credits |
|---|---|---|
| CS 627 | Artificial Intelligence | 3 |
| or CS 727 | Advanced Artificial Intelligence | |
| CS 677 | Machine Learning | 3 |
| or CS 755 | Advanced Pattern Recognition and Machine Learning | |
| Total Credits | 6 | |
Students will choose courses from the list of AI and Analytics Electives.
| Code | Title | Credits |
|---|---|---|
| Elective 1 | 3 | |
| Elective 2 | 3 | |
| Elective 3 | 3 | |
| Total Credits | 9 | |
Students will choose courses from the list of Psychology Electives.
| Code | Title | Credits |
|---|---|---|
| Elective 1 | 3 | |
| Elective 2 | 3 | |
| Total Credits | 6 | |
Select 3 or 6 credit hours of 600-level and above Computer Science and Information Systems courses, 3 credits if thesis option is chosen, 6 credits if capstone project option is chosen.
| Code | Title | Credits |
|---|---|---|
| Seidenberg Elective 1 | 3 | |
| Seidenberg Elective 2 (unless student is completing Thesis I & II) | 3 | |
| Total Credits | 6 | |
Students are required to select one of the following options, the Analytics Capstone Project (3 credits - last semester) or Thesis (6 credits, last 2 semesters). Pursuing a thesis requires the student to work on a research project under the supervision of a professor.
| Code | Title | Credits |
|---|---|---|
| CS 668 | Analytics Capstone Project | 3 |
| Seidenberg Elective | 3 | |
| OR | ||
| CS 693 | Thesis I | 3 |
| CS 694 | Thesis II | 3 |
Total Credits: 30
Please note that the AI and Analytics electives as well as the Seidenberg electives lists will evolve based on new offerings.
| Code | Title | Credits |
|---|---|---|
| AI AND ANALYTICS ELECTIVES | ||
| CS 619 | Data Mining | 3 |
| CS 631V | Topic: Intelligent Agents | 3 |
| CS 632Q | Topic: Introduction to Natural Language Processing | 3 |
| CS 655 | Pattern Recognition | 3 |
| CS 671 | Computer Vision | 3 |
| CS 672 | Introduction to Deep Learning | 3 |
| CS 675 | Introduction to Data Science | 3 |
| CS 676 | Algorithms for Data Science | 3 |
| CS 696C | Topic: AI Ethics | 3 |
| CS 727 | Advanced Artificial Intelligence | 3 |
| CS 740 | Advanced Computer Vision | 3 |
| CS 755 | Advanced Pattern Recognition and Machine Learning | 3 |
| CS 619 | Data Mining | 3 |
| CS 631V | Topic: Intelligent Agents | 3 |
| CS 632Q | Topic: Introduction to Natural Language Processing | 3 |
| CS 655 | Pattern Recognition | 3 |
| CS 671 | Computer Vision | 3 |
| CS 672 | Introduction to Deep Learning | 3 |
| CS 696G | Topic: Generative AI | 3 |
| PSYCHOLOGY ELECTIVES | ||
| PSY 612 | Neuropsychology | 3 |
| PSY 617 | Human Learning | 3 |
| PSY 624 | Cognitive Psychology | 3 |
| SEIDENBERG ELECTIVES | ||
| CS 601C | Computational Statistics | 3 |
| CS 604 | Computer Systems and Concepts | 3 |
| CS 610 | Introduction to Parallel Computing | 3 |
| CS 612 | Concepts and Structures in Internet Computing | 3 |
| CS 617 | Game Programming | 3 |
| CS 623 | Database Management Systems | 3 |
| CS 629 | Computer Graphics | 3 |
| CS 633 | Data Communications and Networks | 3 |
| CS 634 | Computer Networking and the Internet | 3 |
| CS 639 | Mobile Application Development | 3 |
| CS 641 | Mobile Web Content and Development | 3 |
| CS 663 | Human Factors and Usability Metrics | 3 |
| CS 667 | Practical Data Science | 3 |
| CS 673 | Scalable Databases | 3 |
| IS 614 | Applied Artificial Intelligence | 3 |
| IS 641 | Information Security and Controls | 3 |
| IS 668 | Foundation of Geographic Information Systems | 3 |
| IS 679 | Cognitive Science and Technology | 3 |
| IS 684 | Web Mining | 3 |
| IS 685 | Ethical Issues in Artificial Intelligence | 3 |
| IS 687 | Social and Collaborative Computing | 3 |
| IS 689 | Human-AI Interaction | 3 |
| IS 690E | Topic: Information Architecture | 3 |
| IS 690J | Topics: Virtual and Immersive Experience Design | 3 |
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2022-2023 Undergraduate Catalog
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