Foundations of AI and Machine Learning

This unit introduces the historical evolution and foundational concepts of artificial intelligence and machine learning, covering key innovators, milestones, AI definitions, learning processes, neural networks, and common deep learning models. It connects historical cycles and economic drivers to present-day AI applications and prepares students with conceptual grounding for hands-on modeling and ethical evaluation in subsequent units.

Learning Objectives

• Analyze major historical milestones and innovators (from Babbage to modern AI) and map their influence on contemporary AI paradigms and research directions.
• Demonstrate understanding of core terminology and concepts by defining AI, machine learning, neural networks, and common deep learning models and describing how these elements interrelate.
• Apply foundational machine learning workflow steps — data preparation, model training, evaluation, and iteration — to a simple case study to show how machines learn in practice.
• Evaluate the societal and economic drivers of AI, including the causes and consequences of AI winters and revivals, to assess implications for ethical practice, policy, and career pathways in the field.