Foundations of Artificial Intelligence

This unit introduces core concepts that underpin modern AI systems, including definitions of AI, data sources, algorithms, hardware, learning paradigms, neural networks, and generative models. Through domain examples (communication, healthcare monitoring, robotics, transportation, and space) and historical and ethical context, students prepare to apply foundational knowledge in subsequent, more technical units.

Learning Objectives

• Analyze foundational AI concepts by identifying and explaining the roles of data, algorithms, and hardware in at least three real-world AI examples
• Apply data literacy techniques to locate, categorize, and describe the suitability of multiple data sources for AI tasks using written summaries and simple dataset profiles
• Demonstrate understanding of machine learning approaches and neural network structure by comparing supervised, unsupervised, reinforcement, and generative models and producing a diagram that maps model types to example applications
• Evaluate proposed AI applications across domains for feasibility, limitations, and human-centered implications, producing recommendations that address technical constraints and ethical considerations