BSAIML · 30 credits · 10 courses · 7-week format

B.S. in Artificial Intelligence and Machine Learning

A 30-credit, ten-course program that moves students from AI foundations and programming through model development, evaluation, responsible AI, deployment, and an integrative capstone.

30

Total credits

10

Courses to complete

3

Credits per course

7

Weeks per course

Program learning outcomes

What a graduate can do

  • Explain core AI and machine-learning concepts and select appropriate methods for a problem.
  • Prepare data and engineer features for supervised and unsupervised learning.
  • Build, train, and evaluate models using industry-standard tools and workflows.
  • Apply responsible-AI, privacy, and security principles across the model lifecycle.
  • Deploy and monitor models and communicate results to technical and non-technical audiences.
  • Produce a professional capstone artifact demonstrating integrated, independent work.

Coverage

Disciplinary areas

AI foundationsMachine learningData preparationProgrammingModel developmentModel evaluationResponsible AIPrivacy & securityDeployment / MLOpsHuman-centered designGenerative AICapstone

Curriculum

Course inventory

8 core courses plus electives and a capstone. Students complete ten three-credit courses for 30 credits; the approved inventory is larger to support elective choice.

AI 1000 Foundations of Artificial Intelligence
The model course: what AI is, its history, capabilities, limits, and the landscape of methods and tools.
Core · 3 cr
AI 1100 Programming for AI
Python for data and modeling — environments, libraries, notebooks, and reproducible workflows.
Core · 3 cr
AI 1200 Data Preparation and Feature Engineering
Sourcing, cleaning, transforming, and engineering data into model-ready features.
Core · 3 cr
AI 2000 Machine Learning Concepts and Methods
Supervised and unsupervised learning, model families, and the bias–variance tradeoff.
Core · 3 cr
AI 2100 Model Development and Training
Building and training models, tuning, and managing experiments end to end.
Core · 3 cr
AI 2200 Model Evaluation and Validation
Metrics, validation strategy, error analysis, and honest reporting of performance.
Core · 3 cr
AI 2300 Responsible AI, Ethics, and Privacy
Fairness, transparency, accountability, privacy, and governance across the lifecycle.
Core · 3 cr
AI 3100 Deployment and MLOps
Packaging, serving, monitoring, and maintaining models in production environments.
Core · 3 cr
AI 3300 Generative AI and Large Language Models
Foundation models, prompting, retrieval, evaluation, and safe application patterns.
Elective · 3 cr
AI 3400 Applied AI for Regulated Industries
Deploying AI where privacy, security, and regulatory constraints govern the work.
Elective · 3 cr
AI 3500 Human-Centered AI Design
Designing AI products around how people understand, trust, and adopt them.
Elective · 3 cr
AI 4000 AI/ML Capstone
An integrative, professional project defended before faculty and documented as a portfolio artifact.
Capstone · 3 cr

Each course licenses with complete documentation, seven weekly modules, three-hour synchronous session plans, asynchronous activities, labs or cases, assignments, rubrics, and instructor & student guides. See individual courses.

Available for licensing and customization

Adopt it as-is, or adapt it to your institution

This program is available for institutional licensing. It may be adopted as a complete curriculum or customized for your academic model, students, faculty, delivery schedule, modality, policies, branding, technology environment, accreditation needs, and regional or industry context.

The licensing package may include the program overview and rationale, program learning outcomes, curriculum structure and maps, required and elective courses with sequencing and prerequisites, complete course documentation, seven-week instructional modules, synchronous session plans, asynchronous activities, laboratories and case studies, assignments and rubrics, instructor and student guidance, certification and career mapping, capstone documentation, and implementation and quality-assurance materials.

License what already exists. Customize it for your institution. Commission something new.