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 gained knowledge about performing accurate cluster analysis in the MATLAB environment. Our exploration began by understanding the measurement of similarity, including concepts such as element proximity, similarity, and dissimilarity measures. We delved into different methods for grouping objects, namely hierarchical clustering, and partitioning clustering.

Regarding partitioning clustering, we focused on the k-means method. We learned how to iteratively locate k centroids, each representing a cluster. We also examined the effectiveness of cluster separation and how to generate a silhouette plot using cluster indices obtained from k-means. The silhouette value for each data point serves as a measure of its similarity to other points within its own cluster, compared to points in other clusters. Furthermore, we delved into k-medoids clustering, which involves identifying the centers of clusters using medoids instead of centroids. We learned the procedure...