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)
26
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27
Index

Bayesian networks

A Bayesian network is a probabilistic model represented by a direct acyclic graph G = {V, E}, where the vertices are random variables Xi, and the edges determine a conditional dependence among them. In the following diagram, there's an example of a simple Bayesian network with four variables:

Example of Bayesian network

The variable X4 is dependent on X3, which is dependent on X1 and X2. To describe the network, we need the marginal probabilities P(X1) and P(X2) and the conditional probabilities P(X3 | X1, X2) and P(X4 | X3). In fact, using the chain rule, we can derive the full joint probability as:

The previous expression shows an important concept: as the graph is direct and acyclic, each variable is conditionally independent of all other variables that are not successors given its predecessors. To formalize this concept, we can define the function Predecessors(Xi), which returns the set of nodes that influence Xi directly, for example, Predecessors...