Supervised, unsupervised and reinforcement learning
Tags: Machine Learning, Supervised Learning, Unsupervised Learning, Reinforcement Learning

WHAT IS UNSUPERVISED LEARNING?

Unsupervised Learning as the name suggests presence of no supervision. In this type of machine learning machines are trained using training data that is not “labelled”. The objective of unsupervised learning is to restructure the input data into new features or a group of objects with similar patterns.

In unsupervised learning, we do not have a predetermined result. The machine tries to find useful inferences from the huge amount of data.

There are two categories of unsupervised learning:

Clustering — Clustering is a method of grouping similar data items into clusters. It determines the similarities between the data items and categorizes them as per the presence and absence of those similarities. Association — This algorithm is based on finding associations among data items in a large dataset. It determines which data items come together in the dataset.

EXAMPLE OF UNSUPERVISED LEARNING

Suppose you are a die-hard cricket fan. Now one of your friends has asked you to join him at his house for watching a football match between Barcelona and Real Madrid. You have no idea about what football is but just for your friends, you say yes and head over to his house. The match starts and you sit there with no idea with what is going on in the match. Your friends are enjoying the way Lionel Messi plays and want you to join in the fun. Here is when you try to learn about the game. You examine the screen and come up with certain conclusions that you can use to understand the game better.

• There are 2 teams with jerseys of different color combinations. One which has the color combination red-blue and other with white color. Since the last goal was scored by Messi and you saw an update in the score of Barcelona you conclude that the jersey of red-blue belongs to Barcelona and the white jersey belongs to Real Madrid.

• You can see that on the ground there are total of 11 players from each team and out of the 11, 1 player is near the goal, preventing the opponent team member from scoring, making him a goalkeeper and the remaining 10 players are on the field trying to goal in the other’s goal, making them the outfield players.

• If the ball passes over the goal line the team gets few points.

• Messi has a number 10 on his back and his name is written on the back of his t-shirt and if he or any of his fellow team players hits the goal, you need to cheer

• If the opponent team hits a goal you need to show your disappointment.

You make these observations one-by-one and you know when to do what. From knowing nothing to knowing the basics of football, you can now enjoy the match with your friends, without them knowing that you did not know anything.

What happened here?

You had every material that you needed to learn about the basics of football- TV, when and who your friends cheer for. This made you learn about football all by yourself without someone guiding you about anything. This is the principle that unsupervised learning follows.

Did you see how unsupervised learning helped you in real life. In addition to this it has various advantages in the field of machine learning. Let us move to that now and will be also discussing about its disadvantages

ADVANTAGES OF UNSUPERVISED LEARNING

It makes the machine tackle problems that humans might find difficult either due to a limited capacity or a bias. It is ideal for exploring raw and unknown data.

DISADVANTAGES OF UNSUPERVISED LEARNING

The results produced by the unsupervised method are considered to be less accurate and reliable in comparison to the results produced by other techniques of machine learning. The user needs to spend time interpreting and labelling the groups/classes which are formed as result.

APPLICATIONS OF UNSUPERVISED LEARNING

Some of it’s important application are: