Foundations of Artificial Intelligence

This unit establishes core concepts, history, and building blocks of artificial intelligence, including data, algorithms, learning methods, neural networks, and common application types. Students will connect technical foundations to real-world uses and limitations, preparing them for deeper design, development, and ethical decision-making in subsequent units.

Learning Objectives

• Analyze and compare core AI concepts, types, historical milestones, and learning approaches (supervised, unsupervised, reinforcement, and generative) to explain how they relate to different application domains
• Apply data literacy skills to locate, assess, and prepare datasets—identifying quality, bias, privacy, and suitability—for AI tasks such as monitoring, autonomous systems, and communication tools
• Demonstrate how common algorithms, neural network structures, and standard hardware map to real-world AI applications (e.g., robotics, patient monitoring, self-driving features, and space operations) and identify scenarios where AI is unsuitable
• Evaluate human-centered, safety, and societal implications of AI deployment—considering ethics, limits of current state-of-the-art, and strategies to preserve meaningful human roles—and recommend mitigation approaches