Questions:

$1.$ Support Vector Machines $($SVM$)[1$ Marks$]$

Question $1\mathrm{.}1$: Explain the basic concept of SVM$.$ What are the roles of hyperplanes, margins, and kernel functions in SVM$?$

$2.$ Clustering Techniques $[1\mathrm{.}5$ Marks$]$

Question $2\mathrm{.}1$: Explain the principle of K$-$Means Clustering and how it determines the number of clusters in a dataset.

Question $2\mathrm{.}2$: When should we use Clustering techniques?

$3.$ Neural Networks $[1\mathrm{.}5$ Marks$]$

Question $3\mathrm{.}1$: What is a Perceptron and how does it function as the basic building block of neural networks?

Question $3\mathrm{.}2$: Explain the concept of Multilayer Perceptrons $($MLP$).$ How do they differ from simple perceptrons?

$4.$ Ensemble Methods $[1$ Marks$]$

Question $4\mathrm{.}1$: Define and differentiate between Bagging and Boosting in ensemble learning.