Engineering AI (Minor)

Explain core AI concepts, including learning, inference, representation, and the limitations of AI systems.

Analyze and interpret model performance using appropriate metrics, and diagnose common failure modes (e.g., bias, overfitting, drift).

Identify ethical, legal, and social risks of AI and propose mitigations (privacy, fairness, safety, and responsible use).

Communicate AI methods and results clearly to both technical and non-technical audiences.

Apply human-centered design principles to AI systems, including usability, accessibility, transparency, and trust.

Implement and evaluate AI models, including basic neural network architectures, using standard tools and libraries.

Design AI solutions that integrate data pipelines, versioning, testing, and deployment considerations appropriate for real-world use.