Chapter 1, Exploring MATLAB for Machine Learning, covers machine learning, which is a branch of artificial intelligence that is based on the development of algorithms and mathematical models, capable of “learning” from data and autonomously adapting to improve their performance according to the objectives set. Thanks to this learning ability, machine learning is used in a wide range of applications, such as data analysis, CV, text translation, speech recognition, medical diagnosis, and financial risk prediction. Machine learning is an ever-evolving area of research and is revolutionizing many fields of science and industry. The aim of this chapter is to provide some introduction, background information, and a basic knowledge of MATLAB tools. In addition, the basic concepts of machine learning will be introduced.

Chapter 2, Working with Data in MATLAB, looks at how to import and organize our data in MATLAB. Today, the amount of data generated is enormous; smartphones, credit cards, televisions, computers, home appliances, sensors, domestic systems, public and private transport, and so on are just a few examples of devices that generate data seamlessly. Such data is stored and then used for various purposes. One of these is data analysis using machine learning algorithms. To import and organize our data in MATLAB, you should familiarize yourself with the MATLAB workspace to make the operations as simple as possible. Then, we will analyze the different formats available for the data collected and how to move data in and out of MATLAB. We will also explore datatypes to work with grouping variables and categorical data and how to export data from the workspace, including cell array, structure array, and tabular data, and save it in a MATLAB-supported file format. Finally, we will understand how to organize data in the correct format for the next phase of data analysis.

Chapter 3, Prediction Using Classification and Regression, shows us how to classify an object using nearest neighbors and how to perform an accurate regression analysis in a MATLAB environment. Classification algorithms return accurate predictions based on our observations. Starting from a set of predefined class labels, the classifier assigns each input data a class label, according to the training model. Regression relates a set of independent variables to a dependent variable. Through this technique, it is possible to understand how the value of the dependent variable changes as the independent variable varies.

Chapter 4, Clustering Analysis and Dimensionality Reduction, explores clustering methods, which are designed to find hidden patterns or groupings in a dataset. These algorithms identify a grouping without any label to learn from through the selection of clusters, based on the similarity between the elements. Dimensionality reduction is the process of converting a set of data with many variables into data with lesser dimensions but ensuring similar information. Feature selection approaches try to find a subset of the original variables. Feature extraction reduces the dimensionality of the data by transforming it into new features. This chapter shows us how to divide the data into clusters, or groupings of similar items. We’ll also learn how to select a feature that best represents the dataset.

Chapter 5, Introducing Artificial Neural Networks Modeling, delves into artificial neural networks (ANNs), which include data structures and algorithms for the learning and classification of data. Through the neural network techniques, a program can learn by example and create an internal structure of rules to classify different inputs. MATLAB provides algorithms, pretrained models, and apps to create, train, visualize, and simulate ANNs. In this chapter, we will see how to use MATLAB to build an ANN-based model to predict values and classify data.

Chapter 6, Deep Learning and Convolutional Neural Networks, examines deep learning, which is a machine learning technology based on multilayer ANNs and has allowed many applications to reach a high degree of accuracy. Deep neural networks are capable of modeling complex relationships between input and output data. Among the most successful applications is computer vision, with tasks that include classification, image regression, and object detection. For example, a deep neural network is able to generate a layered representation of objects in which each object is identified by a set of characteristics that has the form of visual primitives, such as particular edges, oriented lines, textures, and recurring patterns. Convolutional networks are characterized by convolutional layers, which use filters to analyze data in a local region and produce an activation map. These activation maps are then processed by pooling layers, which aggregate the low-resolution data to reduce the dimensionality of the representation and make processing more computationally efficient. The convolutional and pooling layers are then alternated several times until an image is represented by a low-resolution activation map. In this chapter, we will learn the basic concepts of deep learning and discover how to implement an algorithm based on convolutional networks in the MATLAB environment.

Chapter 7, Natural Language Processing Using MATLAB, explores natural language processing (NLP), which automatically processes information conveyed through spoken or written language. This task is fraught with difficulty and complexity, largely due to the innate ambiguity of human language. To enable machine learning and interaction with the world in ways typical of humans, it is essential not only to store data but also to teach machines how to translate it simultaneously into meaningful concepts. As natural language interacts with the environment, it generates predictive knowledge. In this chapter, we will learn the basic concepts of NLP and how to build a model to label sentences.

Chapter 8, MATLAB for Image Processing and Computer Vision, covers computer vision, which is a field that studies how to process, analyze, and understand the contents of visual data. In image content analysis, we use a lot of computer vision algorithms to build our understanding of the objects in an image. Computer vision covers various aspects of image analysis, such as object recognition, shape analysis, pose estimation, 3D modeling, and visual search. Humans are good at identifying and recognizing things around them! The goal of computer vision is to accurately model the human vision system using computers. In this chapter, we will understand the basic concepts of computer vision and how to implement a model for object recognition, using MATLAB.

Chapter 9, Time Series Analysis and Forecasting with MATLAB, delves into time series data, which is basically a sequence of measurements that are collected over time. These measurements are taken with respect to a predetermined variable and at regular time intervals. One of the main characteristics of time series data is that the ordering matters. The list of observations that we collect is ordered on a timeline, and the order in which they appear says a lot about underlying patterns. If you change the order, this will totally change the meaning of the data. Sequential data is a generalized notion that encompasses any data that comes in a sequential form, including time series data. In this chapter, we will learn the basic concepts of sequential data and how to build a model that describes the pattern of the time series or any sequence in general.

Chapter 10, MATLAB Tools for Recommender Systems, examines the recommendation engine, which is a model that can predict what a user may be interested in. When we apply this to the context of movies, for example, this becomes a movie recommendation engine. We filter items in our database by predicting how the current user might rate them. This helps us in connecting the user to the right content in our dataset. Why is this relevant? If you have a massive catalog, then the user may or may not find all the content that is relevant to them. By recommending the right content, you increase consumption. Companies such as Netflix heavily rely on recommendations to keep the user engaged. In this chapter, we will learn the basic concepts of recommender systems and how to build a movie recommendations system, using MATLAB.

Chapter 11, Anomaly Detection in MATLAB, teaches you the basic concepts of an anomaly detection system and how to implement one in MATLAB. A physical system, in its life cycle, can be subject to failures or malfunctions that can compromise its normal operation. It is, therefore, necessary to introduce an anomaly detection system within the capability of preventing critical interruptions. This is called a fault diagnosis system and can identify the possible presence of a malfunction within the monitored system. The search for the fault is one of the most important and qualifying maintenance intervention phases, and it is necessary to act in a systematic and deterministic way. To carry out a complete search for the fault, it is necessary to analyze all the possible causes that may have determined it.