Book Image

MATLAB for Machine Learning - Second Edition

By : Giuseppe Ciaburro
Book Image

MATLAB for Machine Learning - Second Edition

By: Giuseppe Ciaburro

Overview of this book

Discover why the MATLAB programming environment is highly favored by researchers and math experts for machine learning with this guide which is designed to enhance your proficiency in both machine learning and deep learning using MATLAB, paving the way for advanced applications. By navigating the versatile machine learning tools in the MATLAB environment, you’ll learn how to seamlessly interact with the workspace. You’ll then move on to data cleansing, data mining, and analyzing various types of data in machine learning, and visualize data values on a graph. As you progress, you’ll explore various classification and regression techniques, skillfully applying them with MATLAB functions. This book teaches you the essentials of neural networks, guiding you through data fitting, pattern recognition, and cluster analysis. You’ll also explore feature selection and extraction techniques for performance improvement through dimensionality reduction. Finally, you’ll leverage MATLAB tools for deep learning and managing convolutional neural networks. By the end of the book, you’ll be able to put it all together by applying major machine learning algorithms in real-world scenarios.
Table of Contents (17 chapters)
Free Chapter
1
Part 1: Getting Started with Matlab
4
Part 2: Understanding Machine Learning Algorithms in MATLAB
9
Part 3: Machine Learning in Practice

Summary

In this chapter, we learned the basic concepts of recommender systems, starting with the definition of these systems and then understanding how the problem is approached. We analyzed the different types of recommender systems: CF, content-based filtering, and hybrid recommender systems.

Next, we saw how to use similarities in the data to identify possible fraudulent uses of credit cards. To do this, we trained a model based on the nearest neighbor algorithm but using a modified version of the traditional k-NN algorithm, where neighbors are given varying weights during the prediction or classification process.

Then, we saw how to implement a NIDS based on ensemble methods in MATLAB. Specifically, we adopted an AdaBoost algorithm to identify intrusions in a LAN network.

Finally, we introduced the techniques of deploying machine learning models regarding model compression. We analyzed the most popular model compression techniques, including pruning, quantization, knowledge...