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Mastering Machine Learning Algorithms

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

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Product type Paperback
Published in Jan 2020
Publisher Packt
ISBN-13 9781838820299
Length 798 pages
Edition 2nd Edition
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Authors (2):
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Giuseppe Bonaccorso Giuseppe Bonaccorso
Author Profile Icon Giuseppe Bonaccorso
Giuseppe Bonaccorso
Giuseppe Bonaccorso Giuseppe Bonaccorso
Author Profile Icon Giuseppe Bonaccorso
Giuseppe Bonaccorso
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Table of Contents (28) Chapters Close

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

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...

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