Understand 3 Key Types of Machine Learning

From fraud detection to image recognition to self-driving cars, machine learning (ML) and artificial intelligence (AI) will revolutionize entire industries. Together, ML and AI change the way we interact with data and use it to enable digital growth.

ML是人工智能的一个子集,使机器能够发展problem-solving models by identifying patterns in data instead of leveraging explicit programming. The learning refers to the training process — the algorithms identify patterns in data and then use those patterns to tweak the model, aiming to provide a more accurate output each time. ML can be supervised, unsupervised or reinforced.

“Through 2022, supervised learning will remain the type of ML utilized most by enterprise IT leaders”

“Most of the current economic value gained from ML is based on supervised learning use cases,” saysSaniye Alaybeyi, Senior Director Analyst, Gartner. “Yet unsupervised learning may be a better fit for certain problems — for example, when the goal is clustering entities and labeled data isn’t available. Reinforcement learning is still limited in its enterprise deployments, but its superior precision and targeting is promising for the future.”

Alaybeyi examines the three types of ML used in enterprise AI programs today and the business problems that each can solve.

Supervised learning

Through 2022, supervised learning will remain the type of ML utilized most by enterprise IT leaders. Supervised learning is effective in many business scenarios, such as fraud detection, sales forecasting and inventory optimization.

Supervised learning works by feeding known historical input and output data into ML algorithms. In each step, after processing each input-output pair, the algorithm alters the model to create an output that is as close as possible to the desired result.

“Supervised learning can be used to make predictions, recognize data or classify it”

For example, a model could be fed data from thousands of bank transactions, with each transaction labeled as fraudulent or not. The model will identify patterns that led to a “fraudulent” or “not fraudulent” output, and over time, learn to more accurately predict whether a given transaction is fraudulent.

Input and output data can be derived from historical data, through simulations or through human data labeling. In cases involving unstructured data, like images, video, audio or text, certain properties or categorizations can serve as output data. Supervised learning can be used to make predictions, recognize data or classify it.

Example use cases for supervised learning include:

• Identifying risk factors for diseases and planning preventive measures
• Classifying whether or not an email is spam
• 预测房价
• Predicting whether or not people will vote for a given candidate.
• Finding out whether a loan applicant is low- or high-risk
• Predicting the failure of mechanical parts in industrial equipment

Read more:The CIO’s Guide to Artificial Intelligence

Unsupervised learning

Unsupervised learning is used to develop predictive models from data that consists of input data without historical labeled responses. For example, a list of customers or a set of unlabeled photos could serve as input data in an unsupervised learning use case.

The most common applications of unsupervised learning are clustering and association problems. Clustering produces a model that groups objects based on certain properties, such as color. Association takes those clusters and identifies rules that exist between them.

Example use cases for unsupervised learning include:

• Grouping customers by purchase behavior
• Identifying associations in customer data; for instance, people who buy a certain style or shoe may also be interested in a certain style of bag.
• Classifying people based on different interests
• Grouping inventories by manufacturing and sales metrics

Unsupervised learning can also be used to prepare data for subsequent supervised learning. This is done by identifying patterns or features that can be used to categorize, compress and reduce the dimensionality of data.