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

Discovering pattern recognition using ANNs

Pattern recognition is a branch of machine learning and AI that focuses on the identification of patterns or regularities in data. It involves the automatic discovery and extraction of meaningful information from datasets, with the aim of categorizing or classifying data into different classes or groups. Overall, pattern recognition plays a crucial role in automating the process of identifying patterns and making decisions based on data, making it a fundamental component of many modern AI systems.

Pattern recognition offers numerous benefits in automating decision-making and handling complex data. However, it also poses challenges related to data quality, interpretability, and computational requirements. To leverage its advantages effectively, practitioners need to carefully design and train models while being aware of potential limitations and biases in the data and algorithms.

The classical approach to pattern recognition uses data...