The College of Engineering and Computer Science is proud to announce a new Master of Science in Artificial Intelligence Science, launching Fall 2026 through the Department of Electrical Engineering & Computer Science. Designed for STEM graduates who want deep, senior-level expertise in AI – with no computer science undergraduate degree required.
U.S. job postings requiring AI skills grew 144% year-over-year as of April 2026, compared to just 7% growth for overall job postings (Bipartisan Policy Center / Lightcast). LinkedIn ranked AI Engineer the #1 fastest-growing job title in the United States in 2025, with postings rising 143% year-over-year. The BLS projects data science employment to grow 36% through 2033, and the U.S. faces a structural talent gap of 1.3 million AI openings with supply covering fewer than half. Workers with AI skills earn wage premiums up to 56% above peers (PwC, 2025).
Program overview
A 30-credit graduate program designed to produce senior-level scientists and engineers with deep algorithmic knowledge of AI — capable of designing, developing, and analyzing AI systems across both software and hardware. Two 500-level foundational courses eliminate prerequisite barriers, making the program accessible to any mathematically mature STEM graduate without requiring a Computer Science undergraduate degree.
The program combines a 12-credit advanced AI core, a 3-credit specialization track (AI hardware or NLP), a 6-credit thesis or capstone, 6 credits of AI electives, and a 3-credit engineering elective.
Core Courses – 12 credits
ASE 510
Formal Foundations of AI
The mathematical language of AI — propositional and predicate logic, set theory, proof methods including induction, linear systems, vector spaces and transformations, orthogonality, eigenvalues and eigenvectors, and principal component analysis. Open to any STEM graduate.
Credits: 3
Semester: 1
ASE 511
Introduction to the Theory and Practice of AI and Machine Learning
Knowledge representation, expert systems, supervised ML and deep neural networks, large language models and their limitations, retrieval-augmented generation (RAG), and AI agents using the Model Context Protocol. Open to any STEM graduate.
Credits: 3
Semester: 1
ASE 665
Introduction to Machine Learning
The mathematical language of AI — propositional and predicate logic, set theory, proof methods including induction, linear systems, vector spaces and transformations, orthogonality, eigenvalues and eigenvectors, and principal component analysis. Open to any STEM graduate.
Credits: 3
Semester: 1
ASE 669
Artificial Intelligence Algorithms
Search, planning, optimization, and adversarial reasoning — the algorithmic backbone of modern AI systems.
Credits: 3
Semester: 1
Specialization track – choose one (3 credits)
AI HARDWARE SPECIALIZATION
AS 660 AI Hardware Design
Build the silicon side of AI: accelerators, neuromorphic chips, FPGA-based systems, and edge inference. For engineers shipping AI to embedded systems, robotics, and IoT.
LLM SPECIALIZATION
CIS 668 Natural Language Processing
Statistical and neural NLP — tokenization, embeddings, transformers, and large language models. For researchers and builders working on language models, search, and dialogue systems.
Capstone or thesis – 6 credits
Students complete either a tho-semester industry-facing capstone or an original master’s thesis, both worth 6 credits.
ASE 669
Artificial Intelligence Algorithms
Search, planning, optimization, and adversarial reasoning — the algorithmic backbone of modern AI systems.
Credits: 3
Semester: 1
ASE 669
Artificial Intelligence Algorithms
Search, planning, optimization, and adversarial reasoning — the algorithmic backbone of modern AI systems.
Credits: 3
Semester: 1
AI electives – choose two (6 credits)
Students select two electives to deepen a research direction or align with career goals:
- CIS 637 Multiagent Systems
- CIS 735 Machine Learning for Security
- CIS 668 Natural Language Processing
- ELE 653 Image and Video Processing
- ELE 715 Robot Manipulators
- ASE 660 AI Hardware Design
- PAI 796 Ethics of Data Science
- ASE 690 Independent Study
Additionally, students complete one 3-credit engineering elective in an AI-related area from the CS, ECE, or mathematics curriculum.
Admission requirements
Applicants should hold a bachelor’s degree in a STEM or engineering field. The GRE is currently optional. International applicants must meet English proficiency requirements.
No Computer Science prerequisite required: The program’s two 500-level bridge courses (ASE 510 and ASE 511) are designed so graduates from mathematics, physics, and engineering fields can enter directly.
Why AI Science at Syracuse
Build for working professionals
No CS prerequisite. Two 500-level bridge courses bring graduates from mathematics, physics, and engineering directly into the program without an additional year of prerequisites.
Industry-facing Options
Two-semester capstone mirrors real project cycles and connects students with employers including Lockheed Martin, Saab, and Hidden Level.
Explosive, documented demand
U.S. AI job postings grew 144% YOY as of April 2026 vs. 7% overall (BPC / Lightcast). AI skills carry a 56% wage premium (PwC, 2025). 1.3 million U.S. AI openings; supply covers fewer than half.
Hardware and software both
Among the first graduate AI programs nationally to offer both software (NLP) and hardware specialization tracks.
Research path available
Students pursuing doctoral work or original research can elect the master’s thesis track, defended before a faculty panel per Graduate School standards.
56% wage premium for AI skills
PwC’s 2025 Global AI Jobs Barometer found workers with AI skills earn up to 56% more than peers in equivalent non-AI roles — making a focused AI graduate credential highly valuable.