Book Image

Mastering Machine Learning Algorithms. - Second Edition

By : Giuseppe Bonaccorso
Book Image

Mastering Machine Learning Algorithms. - Second Edition

By: Giuseppe Bonaccorso

Overview of this book

Mastering Machine Learning Algorithms, Second Edition helps you harness the real power of machine learning algorithms in order to implement smarter ways of meeting today's overwhelming data needs. This newly updated and revised guide will help you master algorithms used widely in semi-supervised learning, reinforcement learning, supervised learning, and unsupervised learning domains. You will use all the modern libraries from the Python ecosystem – including NumPy and Keras – to extract features from varied complexities of data. Ranging from Bayesian models to the Markov chain Monte Carlo algorithm to Hidden Markov models, this machine learning book teaches you how to extract features from your dataset, perform complex dimensionality reduction, and train supervised and semi-supervised models by making use of Python-based libraries such as scikit-learn. You will also discover practical applications for complex techniques such as maximum likelihood estimation, Hebbian learning, and ensemble learning, and how to use TensorFlow 2.x to train effective deep neural networks. By the end of this book, you will be ready to implement and solve end-to-end machine learning problems and use case scenarios.
Table of Contents (28 chapters)
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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...