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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

Material