Page 201 - Ai_V1.0_Class9
P. 201
Unsupervised Learning and Generative Modelling
Unsupervised learning is a type of machine learning where models are trained using data that does not have
labels. This means the model has to find patterns and relationships in the data on its own. Generative modelling
is a specific approach within unsupervised learning that focuses on understanding and modelling how the data
is generated. Generative models try to learn the underlying rules that produce the data, so they can create new
examples that look similar to the original data. In summary, Unsupervised learning is about finding patterns in
unlabelled data, and generative modelling is a method within this type of learning that aims to understand and
replicate how the data is made.
Unsupervised Learning
Output
Input Example that's similar to
Unstructured/Unlabelled dataset Emergent pattern/inherent what's in the dataset
structure
The goal of unsupervised learning is to find patterns, structures, or representations in the data without human
intervention. An unsupervised learning approach works on an unlabelled 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.
Generative Modelling
Generative Modelling do not necessarily require labelled datasets. It can work with unlabelled data to learn the
underlying distribution of the data and can generate structured data from the random noise dataset. So, if
random images are fed as training data for the model it can create relevant output based on the features of the
input data. If there are random images which depict streets, cars, buildings, sky, etc. In a given dataset of street
images, a Generative Modelling can learn to generate new street scenes that look like the ones in the dataset. In
another example, if given a dataset of news articles, a generative model can learn to generate new articles that
resemble the style and content of the training data. Let us take an example.
The following images are given as input to the Generative AI model:
Introduction to Generative AI 199

