MATLAB: What Is Machine Learning?

Using MATLAB®, engineers and other domain experts have deployed thousands of applications for predictive maintenance, sensor analytics, finance, and communication electronics. MATLAB makes the hard parts of machine learning easy with:

Point-and-click apps for training and comparing models
Advanced signal processing and feature extraction techniques
Automatic hyperparameter tuning and feature selection to optimize model performance
The ability to use the same code to scale processing to big data and clusters
Automated generation of C/C++ code for embedded and high-performance applications
All popular classification, regression, and clustering algorithms for supervised and unsupervised learning
Faster execution than open source on most statistical and machine learning computations

Machine learning is a data analytics technique that teaches computers to do what comes naturally to humans and animals: learn from experience. Machine learning algorithms use computational methods to “learn” information directly from data without relying on a predetermined equation as a model. The algorithms adaptively improve their performance as the number of samples available for learning increases.

Machine learning uses two types of techniques: supervised learning, which trains a model on known input and output data so that it can predict future outputs, and unsupervised learning, which finds hidden patterns or intrinsic structures in input data.

Figure 1. Machine learning techniques include both unsupervised and supervised learning.

Mastering Machine Learning: A Step-by-Step Guide with MATLABGet this ebook, download the code, and step through a hands-on machine learning tutorial that helps you master machine learning techniques.

Supervised Learning

Supervised machine learning builds a model that makes predictions based on evidence in the presence of uncertainty. A supervised learning algorithm takes a known set of input data and known responses to the data (output) and trains a model to generate reasonable predictions for the response to new data. Use supervised learning if you have known data for the output you are trying to predict.

Supervised learning uses classification and regression techniques to develop predictive models.

Classification techniques predict discrete responses—for example, whether an email is genuine or spam, or whether a tumor is cancerous or benign. Classification models classify input data into categories. Typical applications include medical imaging, speech recognition, and credit scoring.

Use classification if your data can be tagged, categorized, or separated into specific groups or classes. For example, applications for hand-writing recognition use classification to recognize letters and numbers. In image processing and computer vision, unsupervised pattern recognition techniques are used for object detection and image segmentation.

Common algorithms for performing classification include support vector machine (SVM), boosted and bagged decision trees, k-nearest neighbor, Naïve Bayes, discriminant analysis, logistic regression, and neural networks.

Regression techniques predict continuous responses—for example, changes in temperature or fluctuations in power demand. Typical applications include electricity load forecasting and algorithmic trading.

Use regression techniques if you are working with a data range or if the nature of your response is a real number, such as temperature or the time until failure for a piece of equipment.

Common regression algorithms include linear model, nonlinear model, regularization, stepwise regression, boosted and bagged decision trees, neural networks, and adaptive neuro-fuzzy learning.

Using Supervised Learning to Predict Heart Attacks

Suppose clinicians want to predict whether someone will have a heart attack within a year. They have data on previous patients, including age, weight, height, and blood pressure. They know whether the previous patients had heart attacks within a year. So the problem is combining the existing data into a model that can predict whether a new person will have a heart attack within a year.

Unsupervised Learning

Unsupervised learning finds hidden patterns or intrinsic structures in data. It is used to draw inferences from datasets consisting of input data without labeled responses.

Clustering is the most common unsupervised learning technique. It is used for exploratory data analysis to find hidden patterns or groupings in data. Applications for cluster analysis include gene sequence analysis, market research, and object recognition.

For example, if a cell phone company wants optimize the locations where they build cell phone towers, they can use machine learning to estimate the number of clusters of people relying on their towers. A phone can only talk to one tower at a time, so the team uses clustering algorithms to design the best placement of cell towers to optimize signal reception for groups, or clusters, of their customers.

Common algorithms for performing clustering include k-means and k-medoids, hierarchical clustering, Gaussian mixture models, hidden Markov models, self-organizing maps, fuzzy c-means clustering, and subtractive clustering.

Figure 2. Clustering finds hidden patterns in your data.

How Do You Decide Which Machine Learning Algorithm to Use?

Choosing the right algorithm can seem overwhelming—there are dozens of supervised and unsupervised machine learning algorithms, and each takes a different approach to learning.

There is no best method or one size fits all. Finding the right algorithm is partly just trial and error—even highly experienced data scientists can’t tell whether an algorithm will work without trying it out. But algorithm selection also depends on the size and type of data you’re working with, the insights you want to get from the data, and how those insights will be used.

Figure 3. Machine learning techniques.

Here are some guidelines on choosing between supervised and unsupervised machine learning:

Choose supervised learning if you need to train a model to make a prediction--for example, the future value of a continuous variable, such as temperature or a stock price, or a classification—for example, identify makes of cars from webcam video footage.

Choose unsupervised learning if you need to explore your data and want to train a model to find a good internal representation, such as splitting data up into clusters.

Machine Learning with MATLAB

How can you harness the power of machine learning to use data to make better decisions? MATLAB makes machine learning easy. With tools and functions for handling big data, as well as apps to make machine learning accessible, MATLAB is an ideal environment for applying machine learning to your data analytics.

With MATLAB, engineers and data scientists have immediate access to prebuilt functions, extensive toolboxes, and specialized apps for classification, regression, and clustering.

MATLAB lets you:

Compare approaches such as logistic regression, classification trees, support vector machines, ensemble methods, and deep learning.
Use model refinement and reduction techniques to create an accurate model that best captures the predictive power of your data.
Integrate machine learning models into enterprise systems, clusters, and clouds, and target models to real-time embedded hardware.
Perform automatic code generation for embedded sensor analytics.
Support integrated workflows from data analytics to deployment.

In this video, you’ll get a summary of what machine learning is. You’ll start by learning about clustering, which helps you segment a collection of things into groups with distinct attributes. You’ll next explore classification, which you’d use for applications like object detection in images, predictive maintenance, and spam detection. Lastly, you’ll hear about regression, which is used to build models that predict a response along a continuum given other features.

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