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A discriminative model models the decision boundary between the classes. A generative model explicitly models
the actual distribution of each class. In final both of them is predicting the conditional probability P (Animal
Features). But both models learn different probabilities.
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
Emergent pattern/inherent
Unstructured/Unlabelled dataset 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.
Reboot
1. Differentiate between Supervised Learning and Unsupervised Learning approach.
2. List any two points to differentiate between real and AI-generated images.
326 Touchpad Artificial Intelligence (Ver. 3.0)-IX

