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# Introduction

As an introduction we will go through Bishop's Chapter 1.

## Concepts

• Supervised learning: Classification vs regression
• discrete vs continuous target space
• Unsupervised learning: clustering vs density estimation
• Joint distribution estimation vs target distribution estimation (predictive distribution) vs target function estimation
• p(x,y) vs p(y|x) vs max p(y|x)
• ML vs MAP vs Bayesian estimation
• Model parameters and hyperparameters
• Priors and hyperpriors
• Marginalization
• Time-series vs instantaneous
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