In the module you will learn about different probabilistic models, how to train them, and what their different strengths and weaknesses are, starting from the simplest models and gradually working our way up.

7.1 Introduction

7.2 Parametric principles and simple models

7.3 Mixture models and the EM algorithm

7.4 Nonparametric density estimation

7.5 Outlook

<aside> ⬅️ 6.1 Kernel Methods

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<aside> ➡️ 7.1 Introduction

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