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includes various techniques from machine learning, deep learning, natural language processing, and
other areas of artificial intelligence.
5. Differentiate between Supervised learning and Unsupervised learning.

Ans.
Feature Supervised Learning Unsupervised Learning
Definition Learns from labeled data Learns from unlabeled data to find patterns or
where input-output pairs groupings.
are provided.
Objective To predict outcomes To discover hidden patterns or intrinsic
based on input features. structures in data.

Data Requirements Requires a labeled dataset Requires an unlabeled dataset with no
to train the model. predefined outputs.

Output Produces predictions that Produces patterns, clusters or associations that
can be evaluated against require interpretation.
true labels.

Example Predicting house prices Segmenting customers based on purchasing
based on features like size behaviour without prior labels.
and location.

6. Explain Classification model with the help of an example.
Ans. In the classification model, data is categorised under different labels according to the parameters defined
in input and then the labels are predicted for the data. The models in supervised learning are designed to
predict categorical outcomes. For example, in the healthcare industry, these models are used to predict
whether a patient has a disease (1) or not (0) based on symptoms and medical history.
7. Define the two types of AI Modelling techniques.
Ans. In general, AI modelling techniques can be broadly classified into two approaches:
a. Rule Based Approach: The Rule based approach is used to build an AI system that works on the basis
of a predefined hierarchy of rules that govern how to transform user input into desired course of
action or automated actions.
b. Learning Based Approach: Learning based approach refers to the model where relationship or
patterns in the dataset are not defined by the developer but learns and generates output on the basis
of its own identification of patterns or trends in the dataset.

8. Convert the following to a perceptron model. Also, suggest a scenario and show the working of the
perceptron model.

Context:
A family deciding to take a vacation.

Factors:
X1: availability of all family members X2: Expenses for the vacation
X3: Weather at the destination X4: Travel and stay arrangements
Ans. Let us assign weights of 0.8, –0.6, 0.7, and 0.6 to the four factors respectively. The bias is assigned as 1.0
and the threshold value is taken as 10.

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