Supervised versus Unsupervised Learning
By: Srinivas Rao
Tags: Machine Learning, Deep Learning, Supervised Learning, Unsupervised Learning, Artificial Intelligence

In this article, we will discuss the basics of machine learning and understand deeply the various subsections of machine learning. The aim is to help you decide which algorithm to use under different scenarios. Let us initially answer some basic questions, such as what is machine learning.

Machine Learning definitions:

Tom Mitchell: A computer program is said to learn from experience ‘E’ with respect to some class of tasks ‘T’ and performance measure ‘P’, if its performance at tasks in ‘T’, as measured by ‘P’, improves with experience ‘E’. Investopedia: Machine learning is the concept that a computer program can learn and adapt to new data without human interference. Machine learning is a field of artificial intelligence (AI) that keeps a computer’s built-in algorithms current regardless of changes in the worldwide economy.

Also Read: What is Machine Learning? How does It Work?

There are various approaches to implementing machine learning. We list the three main approaches:

Supervised Learning Unsupervised Learning Reinforcement Learning

What is Supervised Learning?

Supervised learning algorithms create a mathematical model for a set of data that contains both the inputs and the desired outputs. We train the machine using data which is “labelled.” It means the data tags are available with the required answer. It is like the learning process which takes place in the presence of a supervisor. A supervised learning algorithm learns from labeled training data, helping you to predict outcomes from new data.

What is Unsupervised Learning?

Unsupervised learning algorithms take a set of data that contains only inputs, and identify structure in the data, like grouping or clustering of data points. Unsupervised learning is a machine learning technique, where you need not supervise the model. Instead, you need to allow the model to work on its own to discover information. It mainly deals with the unlabelled data. Unsupervised learning algorithms allow you to perform more complex processing tasks compared to supervised learning.

Also Read: Overfitting and Underfitting in Machine Learning

Examples For Supervised Learning

To give you an example, here is an image of a fruit and I ask you to identify it. You would immediately tell that this is an apple! But how did your brain process this information? If we give a machine this image, would it be able to tell that it’s an apple and it’s not an orange?

In the beginning, no! Because a machine does not know what an apple looks like unless we train the model to recognise it. In Supervised Learning, we give labelled data to the machine to process it and learn from it! What it means is we give the Machine a set of images which are labelled apples and oranges. In short, we give a lot of data/images to the machine telling it this is what an apple looks like and this is what an orange looks like. Now once the machine has studied the features of an apple and an orange, it has broken down that image in different parameters like shape, size, colour, and mapped these features with the specific fruit. Now next time you give the image of an apple without telling the machine and ask it to recognise it, it would have previous data to learn from and immediately tell that it belongs to Apple Category.

Examples for Unsupervised Learning

In Unsupervised Learning, the input data is not labelled. What that means is we feed the machine with images of apples and oranges without telling it which ones are apples and which ones are oranges. Now the machine studies those images, recognises that there is some similarity between how apples look and similarity between how oranges look without knowing they are apples and oranges. So the machine clusters all apples together and all oranges together based on their shape, size and colour. Now when you feed the machine with a new image, and if it is an orange, it immediately recognises that it belongs to the orange cluster by finding the similarity to the data of oranges.

Applications of Supervised Learning

Natural language processing, computer vision, data analytics are filled with supervised learning tasks. The growth in the field of AI is mainly attributed to the field of supervised learning. Machines are getting better at learning what is being taught to them. They do this better than humans. Many tasks that come under this category are as follow:

Image Recognition Image Segmentation Sentiment Analysis Text Prediction on Search Engines

Applications of unsupervised learning

A common application of Unsupervised Learning is the Recommendation System. The purpose of a recommender system is to suggest relevant items to users. These are algorithms aimed at suggesting relevant items to users which can include movies to watch, text to read, products to buy, or anything relevant which makes the customer engage more with the platform. The output variable i.e. the recommended product/content to populate is learned by the machine by categorising the user profile and past behaviour.

Supervised Learning algorithms

K-Nearest Neighbours SVM Linear Regression Logistic Regression Linear Discriminant analysis Decision Trees Naive Bayes Neural Networks

Also Read: Top Applications of Machine Learning

Unsupervised Learning algorithms

Principal component analysis (PCA) K- Means Singular value decomposition Apriori algorithm for association rule learning problems

Recent and Major Publications

Let us look at a few algorithms and papers that have had a huge impact on the AI community.

Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift: A deep learning technique that massively reduced the problem of overfitting and today, we use it by calling a one-line function from frameworks like Keras or PyTorch. Generative adversarial nets: Generative adversarial networks opened a plethora of new opportunities. It has tremendous potential and may guide the development of deep learning through a thorough understanding of the same. How transferable are features in deep neural networks: This paper introduces transfer learning. Today, all major models are trained in large clusters and we just reuse the data. That’s the power this paper gave all of us. ALBERT: A Lite BERT for Self-supervised Learning of Language Representations: The BERT model is one of the best attempts at natural language understanding. The intention behind BERT models is to implement transfer learning in the domain of NLP. Challenging Common Assumptions In The Unsupervised Learning Of Disentangled Representations: This is our pick in the field of unsupervised learning. Enabling machines to understand high-dimensional data and turn that information into usable representations in an unsupervised manner remains a major challenge in machine learning. In this paper, the joint team of researchers from ETH Zurich, the Max Planck Institute for Intelligent Systems, and Google Research prove theoretically that unsupervised learning of disentangled representations is impossible without inductive bias in both the learning approaches being considered and the datasets.

Recent Developments

Coronavirus has been the single biggest challenge that the entire world has had to face together for a very long time. Therefore, many doctors have come forward and made datasets available to the deep learning community. The intention behind the same is to fasten the process of coronavirus detection. There are various approaches taken to tackle the same.

Computer Vision: Using X-Ray reports to identify whether the person is infected with COVID-19 or not Natural Language Processing: Identifying the degree of impact and fatality rates through social media. Analysing Twitter, Facebook, Linkedin feeds to understand the impact of the virus. Natural Language Processing: Understanding medical needs and social sentiment to better handle the situation. South Korea controlled the situation with the help of the above process.

Also Read: 10 Ways How AI is Fighting the Coronavirus

Overall, the two mentioned fields are massive in sizes and anyone who wants to enter the field of machine learning and artificial intelligence should begin with supervised learning. It is more intuitive and easier to grasp. The real essence and massive scope lie in unsupervised learning. It is an unexplored field (relatively), and therefore our understanding of the world partly depends on the quality of research undertaken in the field of unsupervised learning. To help you understand the gravity of the situation, let us end the article with a quote.

“Unsupervised learning is the way most people will learn in the future. You have this model of how the world works in your head and you’re refining it to predict what you think is going to happen in the future.” Mark Zuckerburg, CEO, Facebook Inc.