Machine Learning, Algorithms, New Advancements
While the term machine learning has become a buzzword and catch-all term that is used to describe various different algorithms and techniques, many consumers around the world may wonder which aspects of machine learning enable them to utilize the devices and services they use to function in their everyday lives. With this being said, while machine learning has various uses and applications, there are three primary types of machine learning, and each of these types has its own respective algorithms. These three types of machine learning include supervised learning, unsupervised learning, and reinforcement learning. Moreover, each of these machine learning types makes use of its own algorithms and underlying approaches to solve a particular problem.
As the name suggests, supervised learning is predicated on training a computer algorithm on the basis of labeled data, where a particular response is known. In other words, supervised learning algorithms are designed to learn new data and information by example, as the algorithm functions in a manner that is similar to that of a teacher supervising students within a classroom. As a teacher lecturing students on a given subject has a wealth of knowledge and insight concerning said topic, the teacher can correct the mistakes of their students as they occur. In the context of supervised learning, this teacher takes the form of a target label or value.
To illustrate this point further, consider a small child that is learning about the difference between various animals in their environment. If this child’s parents were looking to teach them about the differences between a dog and a cat, they would likely point these animals out while in public so that the child could gain a better understanding of the difference between dogs and cats. What’s more, the parents would also highlight that all dogs and cats will generally have the same features, as dogs bark and cats meow, and cats will always be small while dogs can vary in size depending on their breed.
After providing the child with these various real-world examples, the parents would then go home and show the child pictures of cats and dogs. If the child is able to identify the two animals based on the training and examples they received, the teaching efforts of their parents would have proven to be successful. Supervised learning algorithms function in a similar fashion, as a software developer looking to create a facial recognition software program using supervised learning algorithms would upload labeled photos of faces for the purposes of training their model.
As these labeled photos would show a human being’s eyes, nose, mouth, lips, etc, the supervised learning algorithm would in time be able to recognize these features in unlabeled photos after being trained on the inputs of the labeled photos. Supervised learning algorithms are ideal for solving problems relating to classification and regression, and some real-world applications of such algorithms include fraud detection and spam filtering. With all this being said, supervised machine learning is currently the most common subbranch of machine learning. However, there are other types of machine learning techniques that are available to software developers and engineers.
In contrast to supervised learning, unsupervised learning is predicated on training a machine learning algorithm on unlabeled data as opposed to labeled data. In keeping with the example of a teacher lecturing a class in the context of supervised learning, unsupervised learning is more akin to a student learning through self-study or education. Instead of using a target label or value, unsupervised learning algorithms function on the basis of clustering analysis or clustering, a machine learning task that is based upon automatically discovering natural patterns or groups within a data set. To provide an example of unsupervised learning, consider again the example of a young child learning about different animals from their parents.
In the context of supervised learning, the children’s parents would provide numerous examples of cats and dogs in the real world, as well as features that these animals will always have. Conversely, if these same parents were looking to teach their child about the different breeds of cats, they would instead allow said child to naturally notice the various cats in their environment, as opposed to pointing such examples out through supervision. In time, the child would gradually be able to make inferences about different breeds of cats, based on their understanding of the general characteristics and features of cats. To this point, some real-world applications of unsupervised learning algorithms include data exploration, targeted advertising and marketing campaigns, and customer segmentation.
The third primary type of machine learning, reinforcement learning, is predicated on the creation of an agent that is taught to behave in a certain environment. When in this environment, the agent will both perform actions as well as see the results of said actions. For each correct action the agent performs within the environment, the agent will receive positive reinforcement, while for every incorrect action they perform, they will receive negative reinforcement. In the context of reinforcement learning, this feedback loop replaces the labeled data and clustering analysis methods that are employed by supervised and unsupervised machine learning algorithms respectively. To provide an example of reinforcement learning, consider a child learning to ride a bicycle.
When a child learns to ride a bicycle, any incorrect actions they perform with regards to effective riding a bicycle will result in them falling off the bike. On the other end of the spectrum, any positive actions they perform with regard to bicycle riding will allow them to continue improving their bike riding skills. Through these various forms of both positive and negative reinforcement, the child will gradually learn what specific actions need to be performed in order to ride their bike in the most efficient manner possible, all while avoiding the injuries that can result from falling off their bike. Some real-world applications of reinforcement learning include various forms of robotics, healthcare, and broadcast journalism.
Through the application of machine learning algorithms, software developers and engineers have been able to create a number of products and services that have become extremely popular with consumers around the world. From voice assistants such as Apple’s Siri and Amazon’s Alexa to applications such as automatic video redaction software, products, and services that utilize machine learning algorithms and techniques are more prevalent than ever before. As such, despite the breadth of products and services that are already available, software developers and engineers will surely continue to develop new products and services as machine learning algorithms continue to be improved upon.