A model can be wrong in exactly two ways: it can be too simple to capture the real pattern (bias), or too sensitive to the particular training sample it happened to see (variance). Diagnosing which one you're facing tells you whether to add capacity or add data — the two most common fixes are not interchangeable.
Geometric intuitions built in two or three dimensions stop being true once you have two hundred features. As dimension grows, volume concentrates in the corners, distances between points converge toward a single value, and "nearest neighbour" stops meaning much of anything.
The Reinforcement Learning Problem described the agent-environment loop informally. The Markov Decision Process (MDP) is the formalism that makes that loop mathematically precise — and precise enough that every algorithm in this section can be stated and analysed against it.
How big should the model be? The classical answer from Bias-Variance Tradeoff — bigger risks overfitting past some point — turns out to be incomplete for the over-parameterised networks that now dominate deep learning, where a second, deeper descent in test error can appear past the point where the classical U-curve says things should be getting worse.
Every model in the classical ML and deep learning sections so far predicts a label or value from an input. A generative model asks a different, harder question: can you produce a new example that looks like it came from the same distribution as the training data? Answering "yes" requires learning something classifiers never need — an actual model of what the data looks like.