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3. Differentiate between supervised and unsupervised learning. Also, name one algorithm and one application of
each learning.
Ans: Supervised Learning Unsupervised Learning
Supervised learning algorithms are trained using labelled Unsupervised learning algorithms are used
data. to analyse and cluster unlabelled datasets.
Input data is provided to the model along with the output. Only input data is provided to the model.
Algorithm: Regression Algorithm: Clustering
Application: Fraud Detection Application: Customer segmentation
4. Explain deep learning.
Ans: Deep learning is an AI function that mimics the brain’s way of processing data and forming patterns to make
decisions. Features of deep learning include:
• The structure of deep learning models is inspired by the neurons and connections in the human brain.
• Neural networks, or Artificial Neural Networks (ANNs), are the core of machine learning.
• These networks consist of node layers, including an input layer, one or more hidden layers, and an output layer.
• If the output of any node exceeds a certain threshold, the node is activated, sending data to the next layer. Otherwise,
no data is passed along.
• When the network has more than three layers, including the input and output layers, it is called a Deep Neural
Network.
5. What is data science? List two real-life examples where data science is used.
Ans: Data science involves studying data to extract valuable insights for businesses. This multidisciplinary field merges
principles and techniques from mathematics, statistics, artificial intelligence, computer engineering, programming,
and analytics to analyse large datasets effectively. For example:
• Your search recommendations and Google Maps history are based on your previous data.
• Amazon’s personalised recommendations are influenced by your shopping habits.
C. Competency-based/Application-based questions:
#Problem Solving & Logical Reasoning
Assertion and Reasoning Questions:
Direction: Questions 1-3, consist of two statements – Assertion (A) and Reasoning (R). Answer these questions by
selecting the appropriate option given below:
a. Both A and R are true and R is the correct explanation of A.
b. Both A and R are true but R is not the correct explanation of A.
c. A is true but R is false.
d. A is false but R is true.
1. Assertion (A): Deep learning is a subset of machine learning.
Reasoning (R): AI includes both ML and DL.
2. Assertion (A): Machine learning is lending its potential to make cyberspace a secure place by tracking monetary
frauds online.
Reasoning (R): Machine learning has improved using new data for once.
3. Assertion (A): Machine learning is an automated process.
Reasoning (R): The algorithm automatically frames the rules from the data.
Ans. 1. b 2. c 3. a
134 Touchpad Artificial Intelligence (Ver. 3.0)-XI

