Applied AI Systems and Ethics

This unit advances foundational AI concepts into applied systems and real-world contexts, covering machine learning models, deep learning, generative AI, multimedia processing, and domain-specific applications such as drones and self-driving vehicles. Students will also examine safety, privacy, accessibility, and societal implications to prepare for integrated system design and deployment in the final unit.

Learning Objectives

• Analyze and compare supervised and deep learning techniques (e.g., decision trees, convolutional methods, specialized processing) and feature-detection approaches to select appropriate models for image, sensor, and classification tasks.
• Apply generative AI methods and automated correction workflows to produce and refine multimedia artifacts while implementing privacy protections, bias mitigation, and accessibility accommodations.
• Demonstrate design, risk assessment, and validation of AI-driven solutions in real-world domains (e.g., drones, self-driving vehicles, urban and exploratory environments) that incorporate safety, data protection, and user-centered requirements.
• Evaluate ethical, societal, and communication challenges related to AI (including misinformation, hype, empathy, and the societal implications of generative models) and propose concrete mitigation and governance strategies.