Applied AI Systems and Ethics

This unit explores practical design, deployment, and evaluation of AI systems across perception, generative modeling, and domain-specific applications (drones, self-driving vehicles, assistive tech). Students learn techniques for model construction and correction, multimodal processing, and creating specialized processing environments while addressing privacy, safety, accessibility, and societal implications of AI.

Learning Objectives

• Analyze and compare machine learning and deep learning approaches (e.g., decision trees, convolutional networks, generative models) for classification, feature detection, and multimodal tasks, using performance, resource, and deployment tradeoffs
• Apply preprocessing, automatic correction, and specialized processing environment techniques to implement image-based feature detection (edge/shape detection), sensory augmentation, and multimedia AI pipelines
• Demonstrate the design of domain-specific AI solutions (drone operations, self-driving systems, assistive technologies, simulated environments), including privacy protections, safety measures, and accessibility accommodations
• Evaluate societal and ethical impacts of generative AI and other models — including misinformation, hype, data mistruths, and potential harms — and propose mitigation strategies and governance controls