Machine Learning

Machine learning is a method of data analysis that automates analytical model building. Using algorithms that iteratively learn from data, machine learning allows computers to find hidden insights without being explicitly programmed where to look.

The iterative aspect of machine learning is important because as models are exposed to new data, they are able to independently adapt. They learn from previous computations to produce reliable, repeatable decisions and results. It’s a science that’s not new – but one that’s gaining fresh momentum.

Because of new computing technologies, machine learning today is not like machine learning of the past. While many machine learning algorithms have been around for a long time, the ability to automatically apply complex mathematical calculations to big data – over and over, faster and faster – is a recent development. Here are a few widely publicized examples of machine learning applications that you may be familiar with:

• The heavily hyped, self-driving Google car? The essence of machine learning.
• Online recommendation offers like those from Amazon and Netflix? Machine learning applications for everyday life.
• Knowing what customers are saying about you on Twitter? Machine learning combined with linguistic rule creation.
• Fraud detection? One of the more obvious, important uses in our world today.

Why the increased interest in machine learning?

Resurging interest in machine learning is due to the same factors that have made data mining and Bayesian analysis more popular than ever. Things like growing volumes and varieties of available data, computational processing that is cheaper and more powerful, and affordable data storage.

All of these things mean it's possible to quickly and automatically produce models that can analyze bigger, more complex data and deliver faster, more accurate results – even on a very large scale. The result? High-value predictions that can guide better decisions and smart actions in real time without human intervention.

One key to producing smart actions in real time is automated model building. Analytics thought leader Thomas H. Davenport wrote in The Wall Street Journal that with rapidly changing, growing volumes of data, "... you need fast-moving modeling streams to keep up." And you can do that with machine learning. He says, "Humans can typically create one or two good models a week; machine learning can create thousands of models a week."

What Is Deep Learning?

Deep learning is a fast-growing area in machine learning research that has achieved breakthroughs in speech, text and image recognition. It’s based on endowing a neural network with many hidden layers, enabling a computer to learn tasks, organize information and find patterns on its own. Recently, SAS took on a classical problem in machine learning research, the MNIST database, a data set containing thousands of images of handwritten digits.

Watch this webinar to learn what we did and what it reveals about the future of deep learning: The Thinking Machine – Experiments in Deep Learning.

Two of the most widely adopted machine learning methods are supervised learning and unsupervised learning. Most machine learning – about 70 percent – is supervised learning. Unsupervised learning accounts for 10 to 20 percent. Semi-supervised and reinforcement learning are two other technologies that are sometimes used.

• Supervised learning algorithms are trained using labeled examples, such as an input where the desired output is known. For example, a piece of equipment could have data points labeled either “F” (failed) or “R” (runs). The learning algorithm receives a set of inputs along with the corresponding correct outputs, and the algorithm learns by comparing its actual output with correct outputs to find errors. It then modifies the model accordingly. Through methods like classification, regression, prediction and gradient boosting, supervised learning uses patterns to predict the values of the label on additional unlabeled data. Supervised learning is commonly used in applications where historical data predicts likely future events. For example, it can anticipate when credit card transactions are likely to be fraudulent or which insurance customer is likely to file a claim.
  
• Unsupervised learning is used against data that has no historical labels. The system is not told the "right answer." The algorithm must figure out what is being shown. The goal is to explore the data and find some structure within. Unsupervised learning works well on transactional data. For example, it can identify segments of customers with similar attributes who can then be treated similarly in marketing campaigns. Or it can find the main attributes that separate customer segments from each other. Popular techniques include self-organizing maps, nearest-neighbor mapping, k-means clustering and singular value decomposition. These algorithms are also used to segment text topics, recommend items and identify data outliers.
• Semi-supervised learning is used for the same applications as supervised learning. But it uses both labeled and unlabeled data for training – typically a small amount of labeled data with a large amount of unlabeled data (because unlabeled data is less expensive and takes less effort to acquire). This type of learning can be used with methods such as classification, regression and prediction. Semi-supervised learning is useful when the cost associated with labeling is too high to allow for a fully labeled training process. Early examples of this include identifying a person's face on a web cam.
• Reinforcement learning is often used for robotics, gaming and navigation. With reinforcement learning, the algorithm discovers through trial and error which actions yield the greatest rewards. This type of learning has three primary components: the agent (the learner or decision maker), the environment (everything the agent interacts with) and actions (what the agent can do). The objective is for the agent to choose actions that maximize the expected reward over a given amount of time. The agent will reach the goal much faster by following a good policy. So the goal in reinforcement learning is to learn the best policy.

What's the difference between data mining, machine learning and deep learning?

The difference between machine learning and other statistical and mathematical approaches, such as data mining, is another popular subject of debate. In simple terms, while machine learning uses many of the same algorithms and techniques as data mining, one difference lies in what the two disciplines predict.

• Data mining discovers previously unknown patterns and knowledge.
• Machine learning is used to reproduce known patterns and knowledge, automatically apply that to other data, and then automatically apply those results to decision making and actions.

The increased power of today's computers has also helped data mining techniques evolve for use in machine learning. For instance, neural networks have long been used in data mining applications. With more computing power, you can create neural networks with many layers. In machine learning lingo, these are called deep neural networks. It's the increased computing power that enables fast processing of many neural network layers for automated learning.

Taking that a step further, artificial neural networks (ANN) are a group of algorithms that are loosely based on our understanding of the brain. ANNs can – in theory – model any kind of relationship within a data set, but in practice getting reliable results from neural networks can be very tricky. Artificial intelligence research dating back to the 1950s has been punctuated by the successes, and the failures, of neural networks.

Today, a new field of neural network research, known as deep learning, is having tremendous success in areas where many artificial intelligence approaches have failed in the past.

Deep learning combines advances in computing power and special types of neural networks to learn complicated patterns in large amounts of data. Deep learning techniques are currently state-of-the-art for identifying objects in images and words in sounds. Researchers are now looking to apply these successes in pattern recognition to more complex tasks such as automatic language translation, medical diagnoses and numerous other important social and business problems.