Master of Science in Applied AI Systems & Strategy

School of Intelligent Systems & Digital Strategy

Now Accepting Applications

Program Overview

A technical–strategic translator program for the age of intelligent systems.

The MS in Applied AI Systems & Strategy prepares technical–strategic translators capable of designing, deploying, securing, and governing intelligent systems in real-world environments. The program integrates machine learning theory, cloud infrastructure, AI security, ethical governance, and measurable business value analysis. Graduates develop portfolio-ready artifacts, industry-recognized certifications, and the ability to translate between engineers, executives, and regulators in rapidly evolving industries.

  • Bridges engineering and executive leadership
  • Security-first, ROI-driven AI deployment
  • Cloud-native and infrastructure-aware
  • Living curriculum reviewed annually

Program Highlights

  • Technical–Strategic Translator model
  • Security-first AI systems design
  • Embedded GitHub portfolio development
  • Cloud-native AI deployment across AWS, Azure, and GCP
  • Built-in certification alignment
  • Industry-facing Capstone Symposium
  • Annual State of AI Systems Colloquium

Curriculum

Prerequisites (2 courses)

AAIS500

Programming for Applied AI

3 credits

Python fundamentals, APIs, Git workflows, and data handling for AI systems.

AAIS501

Commercial AI Systems & Intelligent Tools

3 credits

Applied use of ChatGPT, Claude, Gemini, enterprise AI platforms, prompt engineering, workflow automation, and responsible adoption.

Core Courses (8 courses)

AAIS600

Mathematical Foundations of Machine Learning

3 credits

Linear algebra, probability, optimization, bias-variance tradeoffs, and evaluation metrics.

AAIS601

Machine Learning Systems & Model Development

3 credits

Supervised and unsupervised learning, feature engineering, evaluation frameworks, and applied modeling pipelines.

AAIS602

Large Language Models & Foundation Systems

3 credits

Transformers, RAG systems, fine-tuning, vector databases, guardrails, and LLM evaluation.

AAIS603

MLOps & Production AI

3 credits

Model versioning, CI/CD for ML, monitoring, drift detection, deployment patterns, and cost optimization.

AAIS604

Cloud AI Infrastructure & Secure Deployment

3 credits

AI services in AWS, Azure, and GCP; IAM; containerization; secure data pipelines.

AAIS605

AI Security, Risk & Governance

3 credits

Adversarial ML, model poisoning, prompt injection, privacy risks, and AI risk frameworks.

AAIS620

AI Strategy, ROI & Organizational Adoption

3 credits

AI business cases, vendor evaluation, build-vs-buy analysis, and executive communication.

AAIS621

Ethics of Intelligent Systems

3 credits

Bias, fairness, autonomy, societal impact, and responsible AI governance.

Capstone (1 course)

AAIS690

Applied AI Systems Integration Capstone

3 credits

Student-proposed applied AI project including business justification, architecture design, security assessment, governance analysis, ROI modeling, and formal defense at the Capstone Symposium.

Elective Specializations

Students complete one concentration (two courses).

Cybersecurity (2 courses)

  • AAIS630 – Adversarial Machine Learning & AI Red Teaming
  • AAIS631 – Secure AI Architecture & Defensive Design

Infrastructure (2 courses)

  • AAIS640 – Distributed AI Systems & Scalable Architectures
  • AAIS641 – Edge AI & Hybrid Cloud Deployment

Governance (2 courses)

  • AAIS650 – AI Compliance Engineering & Regulatory Frameworks
  • AAIS651 – AI Audit, Accountability & Risk Modeling

Healthcare (2 courses)

  • AAIS660 – AI in Clinical Systems & Diagnostics
  • AAIS661 – Healthcare Data Governance & Ethical Risk

Financial Systems (2 courses)

  • AAIS670 – AI in Quantitative Finance & Risk Modeling
  • AAIS671 – Algorithmic Decision Systems & Regulatory Oversight

Career Outcomes

  • AI Systems Strategist
  • AI Integration Lead
  • Machine Learning Operations (MLOps) Specialist
  • AI Governance & Risk Advisor
  • Technical AI Program Manager
  • Director of Intelligent Systems

Program Learning Outcomes

Upon successful completion of this program, graduates will be able to:

  1. PLO1: Design applied AI systems that generate measurable business value.
    • AI Systems Architecture
    • Strategic Decision-Making
  2. PLO2: Explain and apply mathematical foundations underlying modern machine learning models.
    • Machine Learning Theory
  3. PLO3: Deploy and monitor AI systems using modern cloud and MLOps practices.
    • Cloud Infrastructure
    • Operational Excellence
  4. PLO4: Assess and mitigate security, privacy, and regulatory risks in AI deployment.
    • AI Security
    • Governance & Compliance
  5. PLO5: Translate technical AI capabilities into executive-level strategy and ROI modeling.
    • Business Strategy
    • Organizational Integration

Assessment Model

Assessment emphasizes applied execution, strategic clarity, and professional communication. Students are evaluated on their ability to design, analyze, and defend AI system implementations in both technical and executive contexts.

Assessment Methods

  • Applied technical projects
  • Architecture diagrams and deployment plans
  • Executive briefing memoranda
  • Risk and governance analyses
  • Capstone defense presentation

Certification Alignment

Exam vouchers included; coursework prepares students for foundational cloud and AI certifications.

Aligned Certifications

  • AWS Certified Cloud Practitioner
  • Microsoft Azure AI Fundamentals
  • Google Cloud Machine Learning Foundations

Program Delivery

  • Asynchronous instruction with structured weekly milestones
  • Modular enrollment structure — no fixed cohorts
  • Dedicated program mentor assigned to each student
  • Portfolio development integrated across coursework

Portfolio Requirement

Students develop a professional Git-based portfolio across the program, including deployed AI systems, architecture diagrams, ROI analyses, governance assessments, and executive briefing artifacts.

Colloquium Requirement

Students must participate annually in the State of AI Systems Colloquium, featuring industry panels, emerging technology analysis, and applied presentations evaluating the current AI landscape.

Admission Requirements

Required

  • Bachelor's degree from an accredited institution
  • Statement of purpose
  • Resume/CV

Preferred Qualifications

  • Analytical, technical, or business background
  • Professional experience in technology or operations

Prerequisites

  • Applicants without sufficient preparation may be required to complete 500-level prerequisite coursework.
  • Technical readiness assessment may be required.
AAIS500

Programming for Applied AI

3 credits

Python fundamentals, APIs, Git workflows, and data handling for AI systems.

AAIS501

Commercial AI Systems & Intelligent Tools

3 credits

Applied use of ChatGPT, Claude, Gemini, enterprise AI platforms, prompt engineering, workflow automation, and responsible adoption.