Unsupervised learning

Unsupervised learning studies how systems can learn to represent particular input patterns in a way that reflects the statistical structure of the overall collection of input patterns. In unsupervised learning, the machine receives inputs but obtains neither supervised target outputs ( like supervised learning), nor rewards from its environment (like reinforcement learning )).

will be able to find the structure or relationships between different inputs. Most important unsupervised learning is clustering, which will create different cluster of inputs and will be able to put any new input in appropriate cluster.

It may seem somewhat mysterious to imagine what the machine could possibly learn given that it doesn't get any feedback from its environment. unsupervised learning can be thought of as finding patterns in the data above and beyond what would be considered noise. unsupervised problems are ones in which there’s no identified target variable.

In unsupervised learning, you have access to only input features, and don’t have an associated target variable. So what kinds of analyses can you perform if there’s no target available? The unsupervised learning approach has two main classes:

■ Clustering—Use the input features to discover natural groupings in the data and to divide the data into those groups. Methods: k-means, Gaussian mixture models, and hierarchical clustering.

■ Dimensionality reduction—Transform the input features into a small number of coordinates that capture most of the variability of the data. Methods: principal component analysis (PCA), multidimensional scaling, manifold learning.

Some of the goals of unsupervised learning are as follows:

• Discovering useful structures in large data sets without requiring a target desired output

• Improving learning speed for inputs

• Building a model of the data vectors by assigning a score or probability to each possible data vector

Scenario1

1. You are a kid, you see different types of animals, your father tells you that this particular animal is a dog…after him giving you tips few times, you see a new type of dog that you never saw before - you identify it as a dog and not as a cat or a monkey or a potato.

Scenario2

You go bag-packing to a new country, you did not know much about it - their food, culture, language etc. However from day 1, you start making sense there, learning to eat new cuisines including what not to eat, find a way to that beach etc.

Scenario1 is an example of supervised classification, where you have a teacher to guide you and learn concepts, such that when a new sample comes your way that you have not seen before, you may still be able to identify it.

Scenario2 is an example of unsupervised classification, where you have lots of information but you did not know what to do with it initially. A major distinction is that, there is no teacher to guide you and you have to find a way out on your own. Then, based on *some* criteria you start churning out that information into groups that makes sense to you.

Unsupervised Learning:

• is like learning without a teacher

• the machine learns through observation & find structures in data

Example:

Clustering:A clustering problem is where you want to discover the inherent groupings in the data

• such as grouping customers by purchasing behavior

Association:An association rule learning problem is where you want to discover rules that describe large portions of your data

• such as people that buy X also tend to buy Y