Foundations of AI and Machine Learning

This unit introduces the historical development of artificial intelligence and the foundational concepts that underpin modern AI systems, including definitions of AI, machine learning, and neural networks. Students will explore key milestones, basic machine learning paradigms and common deep learning models, and examine AI’s social and economic impacts to prepare for hands-on model development in subsequent units.

Learning Objectives

• Analyze key historical milestones, innovators, and paradigm shifts from early computing (Babbage, Turing) through the AI winters and revivals to current AI research, identifying how each influenced modern AI approaches
• Demonstrate understanding of core definitions and relationships among artificial intelligence, machine learning, and deep learning by accurately distinguishing terms and summarizing roles of data, algorithms, and models
• Compare and contrast primary machine learning paradigms (supervised, unsupervised, reinforcement) and common neural network architectures (e.g., feedforward, convolutional, recurrent) and match each to typical application scenarios
• Evaluate social, economic, and ethical implications of AI today—including AI’s role in the 21st-century economy and considerations for AI for social good—and propose selection criteria for appropriate AI approaches in civic or social projects