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. Deep learning is a specialized form of machine learning.
With the rise in big data, machine learning has become a key technique for solving problems in areas, such as:
- Computational finance, for credit scoring and algorithmic trading
- Image processing and computer vision, for face recognition, motion detection, and object detection
- Computational biology, for tumor detection, drug discovery, and DNA sequencing
- Energy production, for price and load forecasting
- Automotive, aerospace, and manufacturing, for predictive maintenance
- Natural language processing, for voice recognition applications
More Data, More Questions, Better Answers
Machine learning algorithms find natural patterns in data that generate insight and help you make better decisions and predictions. They are used every day to make critical decisions in medical diagnosis, stock trading, energy load forecasting, and more. For example, media sites rely on machine learning to sift through millions of options to give you song or movie recommendations. Retailers use it to gain insight into their customers’ purchasing behavior.
When Should You Use Machine Learning?
Consider using machine learning when you have a complex task or problem involving a large amount of data and lots of variables, but no existing formula or equation. For example, machine learning is a good option if you need to handle situations like these:
Hand-written rules and equations are too complex—as in face recognition and speech recognition.
The rules of a task are constantly changing—as in fraud detection from transaction records.
The nature of the data keeps changing, and the program needs to adapt—as in automated trading, energy demand forecasting, and predicting shopping trends.
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.
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 machine learning 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.
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 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.
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.
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.
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.
MATLAB lets you:
- Extract features from signals and images using established manual and automated methods, and automatically select features to make models more compact
- Compare approaches such as logistic regression, classification trees, support vector machines, ensemble methods, and deep learning.
- Apply AutoML and other model refinement and reduction techniques to create optimized models
- 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.
- Optimize features by applying various feature engineering techniques
Interesting Machine Learning Applications
Creating Algorithms that Can Analyze Works of Art
Researchers at the Art and Artificial Intelligence Laboratory at Rutgers University wanted to see whether a computer algorithm could classify paintings by style, genre, and artist as easily as a human. They began by identifying visual features for classifying a painting’s style. The algorithms they developed classified the styles of paintings in the database with 60% accuracy, outperforming typical non-expert humans.
The researchers hypothesized that visual features useful for style classification (a supervised learning problem) could also be used to determine artistic influences (an unsupervised problem).
They used classification algorithms trained on Google images to identify specific objects. They tested the algorithms on more than 1,700 paintings from 66 different artists working over a span of 550 years. The algorithm readily identified connected works, including the influence of Diego Velazquez’s “Portrait of Pope Innocent X” on Francis Bacon’s “Study After Velazquez’s Portrait of Pope Innocent X.”
Optimizing HVAC Energy Usage in Large Buildings
The heating, ventilation, and air-conditioning (HVAC) systems in office buildings, hospitals, and other large-scale commercial buildings are often inefficient because they do not take into account changing weather patterns, variable energy costs, or the building’s thermal properties.
BuildingIQ’s cloud-based software platform addresses this problem. The platform uses advanced algorithms and machine learning methods to continuously process gigabytes of information from power meters, thermometers, and HVAC pressure sensors, as well as weather and energy cost. In particular, machine learning is used to segment data and determine the relative contributions of gas, electric, steam, and solar power to heating and cooling processes. The BuildingIQ platform reduces HVAC energy consumption in large-scale commercial buildings by 10–25% during normal operation.
Detecting Low-Speed Car Crashes
With more than 8 million members, the RAC is one of the UK’s largest motoring organizations, providing roadside assistance, insurance, and other services to private and business motorists.
To enable rapid response to roadside incidents, reduce crashes, and mitigate insurance costs, the RAC developed an onboard crash sensing system that uses advanced machine learning algorithms to detect low-speed collisions and distinguish these events from more common driving events, such as driving over speed bumps or potholes. Independent tests showed the RAC system to be 92% accurate in detecting test crashes.