Fundamentals in Artificial Intelligence Graduate Certificate
Program Details
Degree
CertificateAvailable
On CampusApply AI, machine learning and deep learning techniques within a technical area of specialization.
The Fundamentals in Artificial Intelligence Graduate Certificate provides a strong foundation in artificial intelligence, machine learning and deep learning, emphasizing both theoretical principles and practical engineering applications. Buoyed by core AI methodologies and enriched through specialized coursework, the certificate prepares students to apply advanced AI techniques within their technical area of interest to address challenges in data-driven and technology-intensive environments.
Certificate Course Structure
The certificate is structured in two parts:
Two core courses that establish a foundation in artificial intelligence and machine learning must be taken first
One program- or department-specific course focused on applying AI techniques
Core AI Courses (2 Courses Required)
Core Course 1 – Programming / AI Foundations
AAI 551 – Engineering Programming: Python (Applied AI Core), or
CS 515 – Introduction to Computer Science (Computer Science AI Core), or
CS 541 – Artificial Intelligence (Computer Science AI Core)
Students with sufficient programming background may substitute the core course 1 with:
CS 508 – Human-Centered AI
Core Course 2 – Machine Learning
AAI 595 – Applied Machine Learning (Applied AI Core), or
CS 559 – Machine Learning (Computer Science AI Core)
Program-Specific Courses (1 Course Required)
Students complete one department-specific course aligned with their academic discipline or professional interests. This course provides applied, domain-focused experience using AI techniques within a chosen field and connects the certificate pathway to participating programs across engineering, science and business disciplines.
Program administration, admissions and certificate completion are coordinated through the department associated with the selected program-specific course.
Application instructions may vary by participating department.
Biomedical Engineering
Civil Engineering
Chemistry and Chemical Biology
Chemical Engineering / Materials Science
CHE 542 – Data Science in Pharmaceutical Development, or
CHE 543 – Machine Learning in Pharmaceutical Development, or
CHE 685 – AI and Machine Learning for Industrial Applications: Innovations in Rheology and Material Processing
Computer Science
CS 583 – Deep Learning
Electrical and Computer Engineering
Engineering Management / Systems Engineering
EM/SYS 626 – AI and Machine Learning for Systems
Mathematical Sciences
MA 549 - Logical Knowledge Representation and Reasoning, or
MA 610 - Artificial Intelligence in Mathematical Research, or
MA 661 - Dynamic Programming and Reinforcement Learning, or
Mechanical Engineering
Physics
PEP 559 – Machine Learning in Quantum Physics
Software Engineering
SSW 625 – AI for Software Engineering
School of Business
BIA 568 – Management of AI Technologies
Who should consider this certificate?
Undergraduate or graduate students at the Schaefer School, and working professionals seeking specialized expertise in artificial intelligence, machine learning and deep learning, with opportunities to apply these skills within a specific engineering, scientific or business discipline.
Designed with flexibility in mind, the certificate can often fit naturally within existing Stevens degree pathways. Many bachelor’s and master’s programs include free electives, allowing students to incorporate AI coursework into their academic plan while building specialized expertise alongside their primary field of study — often without extending time to degree.
Educational Objectives
The Fundamentals in Artificial Intelligence Graduate Certificate is designed to:
Prepare students for specialization in artificial intelligence, machine learning and deep learning across technical and applied domains.
Enable students to apply AI techniques within their chosen technical discipline to address data-heavy problems.
Support professional and academic advancement in AI-enabled engineering, scientific and technical environments.
Educational Outcomes
Upon completion of the certificate, students will be able to:
Apply machine learning and deep learning tools and software to data analysis, reporting and technical decision-making.
Apply AI methodologies and techniques to solve discipline-specific challenges within their chosen technical field.
Demonstrate the ability to integrate AI approaches into real-world engineering, scientific or technical applications.
COURSE CATALOG COMING SOON.
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