Foundations of Artificial Intelligence

This unit introduces foundational concepts in artificial intelligence including definitions, history, core algorithms, data practices, learning paradigms, neural networks, and generative AI. Students examine common application domains, necessary hardware/software infrastructure, real-world limitations, and human-centered/ethical considerations to prepare for deeper technical design and development in subsequent units.

Learning Objectives

• Analyze key AI concepts, historical milestones, and common application types to categorize systems (e.g., rule-based, machine learning, deep learning, generative) and explain their distinguishing features
• Apply data discovery and preparation techniques to locate, assess, and preprocess datasets and justify selection of algorithmic approaches for specified problem scenarios
• Demonstrate how standard hardware and software components support AI workloads and identify contexts where AI is inappropriate or likely to fail
• Evaluate human-centered, ethical, and domain-specific implications of AI deployments (e.g., healthcare monitoring, robotics, autonomous vehicles, space operations) and propose mitigation or design strategies to address risks