Supervised Learning vs Unsupervised Learning vs Reinforcement Learning

Supervised Learning vs Unsupervised Learning vs Reinforcement Learning

Machine learning models are useful when there is huge amount of data available, there are patterns in data and there is no algorithm other than machine learning to process that data. If any of these three conditions are not satisfied, machine learning models are most likely to under-perform.

Machine learning algorithms find patterns in data and try to learn from it as much as it can. Based on the type of data available and the approach used for learning, machine learning algorithms are classified in three broad categories.

1. Supervised learning
2. Unsupervised learning
3. Reinforcement learning

An abstract definition of above terms would be that in supervised learning, labeled data is fed to ML algorithms while in unsupervised learning, unlabeled data is provided. There is a another learning approach which lies between supervised and unsupervised learning, semi-supervised learning. Semi supervised learning algorithms are given partially labeled data.

Reinforcement learning however is a different type of learning which is based on a reward system. ML model/algorithm is rewarded for each decision it makes during training phase. Reward could be positive as encouragement for a right decision or negative as a punishment for wrong decision.

Let's have a look each of these terms in detail with examples.

What is supervised learning?

Suppose you are present in maths class (yes, maths class. If you don't like maths, you shouldn't be here) and you are given with a problem and its related data and you are asked to solve it for available data. You make an attempt and come up with a wrong answer. Your teacher is a noble person. He/She does not evaluate your solution but show you the correct answer. You compare it with your answer, try to identify where you have made mistakes and try to correct it. You are math class, of course you will get many problems to solve and the process continues.

Same is the case with supervised learning. The data is provided with its labels. Labels are the expected output of the input data which are provided by human. In above example, the correct answer the teacher give you is a label in that case. It is also called actual output.

In supervised learning, each instance in the dataset is labeled with its actual output. Learning algorithm has to find a way to come up with exactly the same or a closely related answer. For example a labeled dataset of vehicle images, each image would have the name of the vehicle in the image. A car image would be tagged with "car", bus image with "bus" and so on.
If you are thinking how a huge amount of data is labeled, visit Amazon Mechanical Turk. It is a program hosted by Amazon in which people are paid for data labeling tasks.

How does the supervised learning approach works?

When a machine learning model process an instance from the dataset and calculates the output for that instance. It is called predicted output. Predicted output is then compared with the label of the instance of dataset. Label is also called actual output. If you have understood the math class example then you might be able to guess the next step. Make a guess.

The next step as you might have guessed is to find the difference between the actual output and predicted output and change the solution accordingly. The illustration below will you understand the process more.

supervised learning

 Supervised learning examples:

• Classification 
• Regression

What is unsupervised learning?

Suppose you are sitting in exam hall and you are given with a problem to solve. Unlike math class, this time you have the problem and input data but you do not have the correct answer. You just solve the problem your way. You have no idea whether you answer is correct or not or how wrong it is.
Same is the case with unsupervised learning. In this learning approach, unlabeled data is given to a machine learning model. There is no actual or expected output. The only output in this case would be the predicted output which the model produces itself. So, the difference between the supervised and unsupervised learning is that in supervised learning, labeled data is provided and in unsupervised learning, unlabeled is handed to the model.

unsupervised learning

 Unsupervised learning examples:

• Clustering
• Pattern and sequence mining
• Image segmentation.

Reinforcement learning

How do we teach kids? We ask them to do something, when they are done we reward them somehow for good results and punish them otherwise. The reward could be anything like a chocolate bar or just verbal encouragement. Operant Conditioning is the word used for this type of learning in biology.
However, in deep learning we call this type of learning approach reinforcement learning. As in the beginning, kids have no idea of how to do a task. They learn from experience. They are more likely to repeat the behavior they are rewarded for and avoid the behavior they are punished for.
Similarly, deep learning models initially have no idea how to perform a task. In reinforcement learning, the model is rewarded or punished each time it makes a correct or wrong prediction during training time.

How does reinforcement learning works?

Although, this type of learning is close to how human and animals learn but to implement it in computers, we use game theory. In game theory we have a set of states that player could be in, a set of actions that player could take to reach to another state and the rewards or points to for each state action pair. We will not go into details here how reinforcement learning works but just grab the point game theory is used in reinforcement learning. Of-course there are other methods too but they are similar to game theory.