Description
The Applied AI Engineering pathway teaches computer science, software engineering, and AI-assisted workflows. Students will learn to design applications, integrate AI techniques, and contribute to open-source projects.
Topics Covered
- Module 1: AI-Native Programming Foundations
- Module 2: Systems and Algorithms in AI Workflows
- Module 3: Data, Models, and Machine Learning Literacy
- Module 4: Applied AI Systems and Collaboration
- Module 5: Intelligent Systems and Professional Showcase
Learning Objectives
- Apply data structures, algorithms, and object-oriented programming within AI-integrated workflows
- Assess, test, and improve AI-generated code using rigorous reasoning and verification
- Build systems that incorporate AI components responsibly and effectively.
- Gain literacy in machine learning concepts, including data representation and model behavior.
- Use professional Git and GitHub workflows to collaborate, document, and contribute to codebases.
- Explore advanced capabilities such as retrieval-augmented generation (RAG), agentic workflows, fine-tuning, and guardrails for reliability and safety.
Requirements
- Python 3.13
- Git + GitHub
- VS Code
- AI Coding Assistants (GitHub Copilot and Claude Code)
- Conversational AI Tools (ChatGPT, Gemini, or Claude)
- pytest