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

Understanding data fitting with ANNs

Data fitting is the process of constructing a curve or mathematical function that best matches a given set of collected data points. This curve fitting can involve either interpolations, where exact data points are fitted, or smoothing, where a smooth function approximates the data. In the context of regression analysis, curve fitting is closely related to statistical inference, considering uncertainties arising from random errors in observed data.

The approximate curves obtained through data fitting have multiple applications. They can be used to visualize and display the data, predict function values in regions with no available data, and summarize the relationships between multiple variables. This process is valuable for understanding and interpreting complex datasets, making predictions, and gaining insights from the collected information.

Predicting the trend of a particular distribution using mathematical formulas can be challenging...