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

MLE and MAP Learning

In many statistical learning tasks, our goal is to find the optimal parameter set according to a maximization criterion. The most common approach is based on the likelihood and is called MLE.

In fact, given a statistical model parametrized with the vector , the likelihood can be interpreted as the probability of such a model generating the training data. Therefore, given a suitable structure of the MLE provides a simple but extremely effective tool to define a generative model that is never biased by prior belief. For our purposes, let's suppose we have a data-generating process pdata, used to draw a dataset X:

In this case, the optimal set that maximizes the likelihood of a generic statistical model parametrized with is found as follows:

This approach has the advantage of being unbiased by incorrect preconditions, because the optimal value depends exclusively on the observed data. However, at the same time, this approach...