Perhaps one of the most promising fields of artificial intelligence (AI) is machine learning (ML). This particular subset allows computers to “learn” to do tasks via examples and experience. For several years now, many research and development (R&D) efforts in the ML space have successfully produced technologies with many real-world applications. Almost everything that you see today, such as programmatic advertising technologies used by digital marketers and automated recommendations from Netflix, are powered by ML algorithms that learn from your past preferences and activities.
But while ML is indeed becoming mainstream, it comes in different forms. In this post, we’ll dig deeper into two types of ML algorithm—supervised and unsupervised learning—and attempt to point out how exactly they differ.
What is Supervised Learning?
Supervised learning is responsible for teaching a machine or system to do tasks using several training methods that follow a desired output or goal. Like its name, the process requires observation and guidance to make sure that tasks or projects push through. Supervised learning is thus best applied to jobs with known outcomes, such as classifying objects. An example of this is training neural networks.
The training process involves feeding a machine with named data, which it will use to predict outcomes. In the case of artificial neural networks (ANNs), the dataset contains the input pattern or information that the system can use to derive their output. That pattern would train the system to achieve a specific output.
So, say you want to develop an ML-powered image classification algorithm that can identify the presence of cars, trucks, and boats. To effectively train the system, it must first have a huge bunch of images of cars, trucks, and boats that it needs to classify before getting fed to the algorithm as inputs. Once done, the pictures with their corresponding annotations would undergo mathematical modeling to map an image to its proper classification appropriately.
What is Unsupervised Learning?
Unsupervised learning, meanwhile, does not require an output pattern. That means the system would not receive any form of training. The learning process ensues by allowing the machine to spot notable characteristics from input patterns on its own. Instead of getting trained, the systems draw conclusions from unlabeled datasets. Just like how humans learn from satisfying their curiosity. Infants, for example, need not be trained to crawl on their own. They learn to do so because they want to satisfy their curiosity (i.e., reaching for a toy).
Unsupervised learning is applied to complex algorithms because there is little to no usable information from datasets. It does so by searching for clusters or groups to obtain an estimate of each kind of object’s number, for instance.
Let’s say that you are running an e-commerce shop and have to deal with troves of customer data. You want to segment your customers according to buying habits so that you can craft marketing strategies effectively. Since you do not have predetermined categories to group them, it would be impossible for you to use supervised ML. That is where unsupervised learning comes in. Since it doesn’t require labeled datasets, a machine can crawl data and divide inputs into groups based on shared characteristics by itself.
What’s the Difference between Supervised and Unsupervised Learning?
In sum, both supervised and unsupervised learning are changing the ML landscape in terms of how systems learn and produce outputs. But while supervised learning requires labeled datasets and a predetermined output, unsupervised learning does not. That said, systems capable of unsupervised learning go through a much more complicated process than those designed for supervised learning. Finally, while supervised learning may process data offline, unsupervised learning requires a constant connection to datasets and potential sources for analysis.
While there are marked differences between supervised and unsupervised learning, there is no doubt that both are required to make even more significant strides in the ML space. As both techniques progress, we’ll see more innovations in the ML and AI fields.