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2. Differentiate between labelled and unlabelled dataset. c. Case Study 3: An autonomous vehicle is learning to navigate through a city environment. It receives feedback in the
form of rewards for reaching its destination safely and penalties for traffic violations. Which type of learning is this
Ans. The difference between labelled and unlabelled dataset are:
case study most likely based on?
Labelled Data: Marked or tagged data, which easily identifiable is called labelled data. For example, name, type,
Ans. Reinforcement Learning (RL)
colour, etc.
d. Case Study 4: A healthcare provider wants to identify patterns in patient data to personalize treatment plans. They
Unlabelled Data: Data that is not marked/tagged is called unlabelled data. It is also known as the raw form of data.
have a dataset with various patient attributes but no predefined labels indicating specific treatment plans. Which
3. What is Rule-based Approach? Write any one drawback of it. type of learning is this case study most likely based on?
Ans. The Rule-based Approach is one of the earliest and simplest methods of implementing artificial intelligence. It relies on Ans. Unsupervised Learning
predefined rules and facts created by developers to enable machines to perform specific tasks and generate desired e. Case Study 5: A manufacturing company wants to optimize its production process by detecting anomalies in sensor
outputs. Developers manually define a set of rules that determine how the machine processes data and responds to data from machinery. They have a dataset with examples of normal and anomalous behaviour. Which type of
various scenarios. learning is this case study most likely based on?
The main drawback of this approach is that the machine's learning is static. Once trained, the machine does not adapt Ans. Supervised Learning
to changes made in the original training dataset. If the machine is tested on a dataset that differs from the rules and 4. Identify the type of model (classification, regression, clustering, association model) are the following case studies most
data provided during the training stage, it will fail to produce accurate results and will not learn or adjust to the new likely based on? [CBSE Handbook]
conditions it encounters. a. A bank wants to predict whether a loan applicant will "default" or "non-default" on their loan payments. They have
a dataset containing information such as income, credit score, loan amount, and employment status.
4. Explain the term clustering. Give an example.
Ans. Classification
Ans. Clustering is a machine learning approach where the machine partitions the dataset into different clusters or categories
b. A real estate agency wants to predict the selling price of houses based on various features such as size, location,
based on machine generated algorithms. The data fed to such a model is usually unlabelled or random and thus the
number of bedrooms, and bathrooms. They have a dataset containing historical sales data.
developer feeds in the data directly into the machine and instructs it to build its own algorithm. The machine then
Ans. Regression
forms a pattern or cluster based on training data and groups those that follow the same pattern.
c. A marketing company wants to segment its customer base into distinct groups based on purchasing behaviour for
The best clustering is the one that minimizes the error. Clustering works on discrete dataset. For example, if you have targeted marketing campaigns. They have a dataset containing information such as purchase history, frequency of
random data of insects and reptiles, since you are unable to find any meaningful pattern amongst them, you would purchases, and amount spent.
feed their data into the clustering algorithm. The algorithm would then analyse the data and divide them into clusters Ans. Clustering model
according to their similarities based on the trends noticed. The clusters are then given as the output. d. A grocery store wants to identify associations between different products purchased by customers to understand
5. Explain Unsupervised Learning approach with an example. which products are commonly bought together. They have a transaction dataset containing records of items
purchased together during each transaction.
Ans. Unsupervised learning approach works on an unlabelled dataset. This means that the data which is fed to the machine
Ans. Association model
is random and there is no knowhow available about it to the model. The machine analyses the data and identifies
patterns, structures, or relationships on its own without any guidance. The goal is to group or organise data based on Unsolved Questions
similarities or differences.
In this model the major features are identified by the machine, which help the user in understanding the data. For SECTION A (Objective Type Questions)
example, in the data of 100 cat images, if you want to understand some pattern in the data, you would need to feed this
uiz
data into the unsupervised learning model and train the machine. Once trained, the machine would identify patterns
in the data. These patterns might already be known to the user, like colour or size, or different features of the cats. A. Tick ( ) the correct option.
C. Competency-based/Application-based questions. 21 st Century #Information Literacy 1. What does Reinforcement Learning aim to maximise? b. Clustering efficiency
Skills
#Critical Thinking
a. Prediction accuracy
1. Emma is using a ridesharing app to book a cab. She notices that the app predicts her destination based on her travel
c. Cumulative rewards d. Data patterns
history and provides an estimated arrival time for the cab. Which technology is most likely responsible for predicting
Emma's destination?
2. Which algorithm is primarily used in image-related tasks like facial recognition?
Ans. Machine Learning
a. Regression b. ANN
2. A hospital uses a system that can automatically detect tumours in X-ray images with high accuracy. The system has
been trained using a large dataset of medical images. Which type of technology is being used in this scenario? c. CNN d. Classification
Ans. Deep Learning using Convolutional Neural Networks (CNNs)
3. Artificial neural networks are inspired by the structure and function of: [CBSE Handbook]
3. Identify the type of learning (supervised, unsupervised, reinforcement learning) are the following case studies most a. The human brain b. Quantum computers
likely based on? [CBSE Handbook]
c. Complex mathematical models d. High-speed processors
a. Case Study 1: A company wants to predict customer churn based on past purchasing behaviour, demographics, and
customer interactions. They have a dataset with labelled examples of customers who churned and those who did not. 4. Training a neural network often requires: [CBSE Handbook]
Ans. Supervised Learning
a. A small set of labelled data samples
b. Case Study 2: A social media platform wants to group users based on their interests and behaviour to recommend
relevant content. They have a large dataset of user interactions but no predefined categories. Which type of learning b. A significant amount of data and computational resources
is this case study most likely based on? c. A specific set of programming instructions
Ans. Unsupervised Learning d. A human expert to guide the learning process
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