Apply the certificate’s core methods with disciplinary judgment
Professional Certificate
Computer Science & Artificial Intelligence
Computer Science & Artificial IntelligenceProfessional Certificate · Computing, Communication & Enterprise
Applied Artificial Intelligence
Machine learning, language and multimodal models, retrieval, tools, evaluation, governance, and operational AI systems.
Program purpose
Professional Certificate
Machine learning, language and multimodal models, retrieval, tools, evaluation, governance, and operational AI systems. The pathway combines academic foundations with evaluated applied work and a portfolio-ready final product.
Learning outcomes
What graduates are prepared to do
Program outcomes connect disciplinary knowledge to methods, judgment, communication, and independently evaluated work.
Document evidence, procedures, quality controls, and limitations
Produce a professional or scholarly output that can be reviewed and reused
Communicate results to the appropriate academic, technical, or public audience
Curriculum architecture
How the pathway is organized
Coursework progresses from foundations to methods, integration, and a culminating demonstration of capability.
Foundations
Build the knowledge, skills, and mindset for inquiry.
Application
Apply methods and tools to real-world questions.
Integration
Synthesize across disciplines and perspectives.
Demonstration
Produce and present work that creates impact.
- 01
Foundational course sequence
- 02
Applied methods and domain integration
- 03
Documented final project or portfolio
Course sequence
Selected curriculum
AI-210
Machine Learning Foundations
Introduces supervised and unsupervised learning, model evaluation, features, data quality, bias, and responsible application.
- Requirement
- Core
- Credits
- 3.0
Applied AI Systems
Builds AI-enabled applications using models, retrieval, tools, evaluation, guardrails, and human review within a documented system architecture.
- Requirement
- Core
- Credits
- 3.0
Data Management & Reproducible Research
Develops durable data structures, metadata, versioning, cleaning, provenance, documentation, and reproducible analysis workflows.
- Requirement
- Core
- Credits
- 3.0
Learning environments
Field, laboratory & studio work
- Course-specific field, laboratory, studio, or computing environments
Professional directions
Where the capability can lead
- Advanced professional practice
- Applied research and technical roles
- Preparation for graduate or continuing study
Culminating work
Capstone, thesis, dissertation, or professional demonstration
A documented portfolio, method package, analysis, report, or applied demonstration integrates the certificate sequence.
Admissions preparation
Materials and background
Admissions review considers preparation, purpose, prior work, and the fit between the applicant’s goals and the program.
Academic or professional preparation appropriate to the field
Statement of interest and intended application
Evidence of prerequisite knowledge where specified
Next step
Connect this pathway to your academic goals.
Request information for a focused conversation or begin an application through the Stella Nova Student Information System.