Page 177 - Robotics and AI class 10
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Unsupervised Learning
An unsupervised learning approach works on an unlabeled dataset. This means that the data which is fed to the
machine is random and there is no know how available about it to the trainer.
These learning models are used to identify trend, pattern and relationship in the data which is fed into it. In this
model the major features are identified by the machine, which helps the user in understanding the data.
For example, in the data of 100 cat images, if you want to understand some pattern in the data, you would need
to feed this data into the unsupervised learning model and train the machine. Once trained, the machine would
identify patterns in the data. These patterns might already be known to the user, like colour or size, or something
unusual about the cats.
Unsupervised Learning can further be divided into:
Clustering
It is a machine learning approach where the machine partitions the dataset into different clusters or categories
based on machine generated algorithms. The data fed to such a model is usually unlabeled or random and thus
the developer feeds in the data directly into the machine and instructs it to build its own algorithm. The machine
then forms a pattern or cluster based on training data and groups those that follow the same pattern.
The best clustering is the one that minimizes the error. Clustering works on discrete dataset. For example, if you have
random data of flowers from your garden, since you are unable to find any meaningful pattern amongst them, you
would feed their data into the clustering algorithm. The algorithm would then analyse the data and divide them into
clusters according to their similarities based on the trends noticed. The clusters are then given as the output.
Dimensionality Reduction
Humans can visualise any figure up to 3-Dimensions only, but according to a lot of theories and algorithms, there
are various entities which exist beyond 3-Dimensions. For example, in Natural language Processing, the words are
considered to be N-Dimensional entities, which means that we cannot visualise them as they exist beyond our
visualisation ability. Hence, to make sense out of it, we need to reduce their dimensions which we do by using
dimensionality reduction algorithm.
As we reduce the dimension of an entity, the information which it contains starts getting distorted. For example, if
we have a ball in our hand, it is 3-Dimensional right now. But if we click its picture, the data transforms to 2-D as an
image which is a 2-Dimensional entity. Now, as soon as we reduce one dimension, at least 50% of the information
is lost as now we will not know about the back of the ball.
Components of AI Project Framework 175

