Artificial Intelligence Machine Learning
AIML 271 - Applied Artificial Intelligence
Description: Develops practical skills for building, deploying, and integrating artificial intelligence systems through hands-on programming. Students implement machine learning pipelines, interact with large language models (LLMs), build retrieval-augmented generation (RAG) systems with vector databases, apply computer vision techniques using pre-trained models, and deploy AI applications as web services. The course bridges the conceptual understanding developed in AI 210 with production-level implementation, emphasizing code quality, error handling, and system integration. Students begin by translating visual workflows into code, then progressively tackle more complex AI engineering challenges: prompt engineering and structured output parsing with local large language models (LLMs), embedding-based document retrieval, transfer learning for custom image classification, and Representational State Transfer (REST) API development for model serving. Projects include a conversational domain expert agent, a complete retrieval-augmented generation (RAG) system grounded in domain-specific documents, and a final project integrating multiple AI techniques. By course completion, students will be able to implement end-to-end AI systems that combine trained models, language model APIs, and software engineering best practices to solve real-world problems. Letter grade only.
Units:
3
No sections currently offered.
Requirement Designation:
Prerequisite: AI 210