From Data to Decisions: How Machine Learning Works

How do supervised, unsupervised, and reinforcement learning differ in how they learn from data? What kinds of problems are best suited to each major type of machine learning? In what ways do training data shape what a model can and cannot learn? How does the choice of labels or lack of labels influence the behavior of an AI system?

What does it mean for an AI system to learn a representation of the world rather than memorizing data? How do concepts like features, embeddings, and latent spaces influence what a model can notice? What is generalization, and why can a model perform well on training data but fail on new cases? How can we test whether a model is truly learning patterns versus exploiting shortcuts in the data?

What are the most common ways machine learning systems fail in real-world use? How do data drift and distribution shift undermine model performance over time? Why can even highly accurate models still be dangerous or unfair? How can we recognize the boundary between what a model can reliably do and where it should not be trusted?