Foundations of AI for Markets

This unit introduces core stock market fundamentals alongside foundational artificial intelligence concepts, capabilities, processes, and emerging algorithms relevant to financial analysis. Students will build the conceptual vocabulary and analytic perspective needed to connect market data to AI-driven prediction workflows and prepare for hands-on modeling and evaluation in subsequent units.

Learning Objectives

• Analyze stock market fundamentals including market structure, security types, price drivers, basic valuation metrics, and how these concepts inform financial-analysis questions
• Demonstrate understanding of artificial intelligence fundamentals by explaining key definitions, capabilities, components, and common use cases relevant to finance
• Apply knowledge of AI processes and emerging algorithms (e.g., supervised/unsupervised learning, reinforcement learning, neural networks, transformers) to map appropriate methods to specific market-analysis tasks
• Evaluate the strengths, limitations, and ethical considerations of using AI tools in financial contexts, citing examples that highlight model risk, data quality, and interpretability concerns