You're reading from Mastering Machine Learning Algorithms Expert techniques for implementing popular machine learning algorithms, fine-tuning your models, and understanding how they work

Product type Paperback

Published in Jan 2020

Publisher Packt

ISBN-13 9781838820299

Length 798 pages

Edition 2nd Edition

Languages

Python

Tools

Keras

Concepts

Machine Learning

Authors (2):

Giuseppe Bonaccorso

View More author details

Table of Contents (28) Chapters

Preface

1. Machine Learning Model Fundamentals

2. Loss Functions and Regularization FREE CHAPTER

3. Introduction to Semi-Supervised Learning

4. Advanced Semi-Supervised Classification

5. Graph-Based Semi-Supervised Learning

6. Clustering and Unsupervised Models

7. Advanced Clustering and Unsupervised Models

8. Clustering and Unsupervised Models for Marketing

9. Generalized Linear Models and Regression

10. Introduction to Time-Series Analysis

11. Bayesian Networks and Hidden Markov Models

12. The EM Algorithm

13. Component Analysis and Dimensionality Reduction

14. Hebbian Learning

15. Fundamentals of Ensemble Learning

16. Advanced Boosting Algorithms

17. Modeling Neural Networks

18. Optimizing Neural Networks

19. Deep Convolutional Networks

20. Recurrent Neural Networks

21. Autoencoders

22. Introduction to Generative Adversarial Networks

23. Deep Belief Networks

24. Introduction to Reinforcement Learning

25. Advanced Policy Estimation Algorithms

26. Other Books You May Enjoy

27. Index

The EM Algorithm

In this chapter, we're going to introduce a very important algorithmic framework for many statistical learning tasks: the Expectation Maximization (EM) algorithm. Contrary to what its name might suggest, this is not an algorithm used to solve a single problem, but a methodology that can be applied in several contexts where the objective of the algorithm is learning the structure of the data-generating process through an iterative and flexible approach. Generative models, for example, are extremely powerful tools that help the data scientist in describing the existing data and generating new data. Unfortunately, direct optimization of these models is often impossible.

The EM algorithm, on the other hand, can often be applied with a minimum of effort. The goal of this chapter is to explain the rationale of this method and show the mathematical derivation, together with some practical examples. In particular, we are going to discuss the following topics...