Advanced Concepts of Modelling in AI – Class 10 Artificial Intelligence NCERT Book Solutions
NCERT/CBSE Book Exercise with Answer
A. Multiple Choice Questions:
Que 1. In which type of machine learning is the data labelled with the desired output?
a) Supervised Learning b) Unsupervised Learning
c) Reinforcement Learning d) Deep Learning
Answer: a) Supervised Learning
2. An email spam filter that learns to identify spam emails based on labeled examples is an application of:
a) Supervised Learning b) Unsupervised Learning
c) Reinforcement Learning d) Transfer Learning
Answer: a) Supervised Learning
Que 3. A machine learning algorithm that groups similar customer purchases into clusters for recommendation systems uses:
a) Supervised Learning b) Unsupervised Learning
c) Reinforcement Learning d) Neural Networks
Answer: b) Unsupervised Learning
Que 4. An AI agent playing a game and learning from its rewards and penalties is an example of:
a) Supervised Learning b) Unsupervised Learning
c) Reinforcement Learning d) Evolutionary Learning
Answer: c) Reinforcement Learning
Que 5. Which of the following statements is NOT true about supervised learning?
a) Requires labeled data for training.
b) Used for classification and regression tasks.
c) Can be less efficient for large datasets.
d) Often used in image recognition applications.
Answer: c) Can be less efficient for large datasets
Que 6. In an unsupervised learning scenario, the goal is to:
a) Predict a specific output based on labeled data.
b) Identify patterns and relationships within unlabeled data.
c) Train an AI agent through rewards and penalties.
d) Develop complex neural network architectures.
Answer: b) Identify patterns and relationships within unlabeled data
Que 7. Clustering algorithms are commonly used in unsupervised learning for:
a) Spam filtering b) Image classification
c) Stock price prediction d) Grouping similar data points
Answer: d) Grouping similar data points
Que 8. Reinforcement learning is particularly useful for scenarios where:
a) Large amounts of labeled data are available.
b) The desired outcome is clear, but the path to achieve it is unknown.
c) The data is structured and easily categorized.
d) The task requires reasoning and logical deduction.
Answer: b) The desired outcome is clear, but the path to achieve it is unknown.
Que 9. Imagine an AI playing a game and learning to win by trial and error. This is an example of:
a) Supervised Learning b) Unsupervised Learning
c) Reinforcement Learning d) Natural Language Processing
Answer: c) Reinforcement Learning
Que 10. Artificial neural networks are inspired by the structure and function of:
a) The human brain b) Quantum computers
c) Complex mathematical models d) High-speed processors
Answer: a) The human brain
Que 11. The process of adjusting the weights in a neural network to improve performance is called:
a) Activation b) Learning c) Optimization d) Training
Answer: d) Training
Que 12. A neural network with multiple layers of interconnected neurons is called a:
a) Single-layer network b) Deep Neural Network
c) Linear network d) Perceptron
Answer: b) Deep Neural Network
Que 13. Neural networks are particularly well-suited for tasks involving:
a) Simple calculations and mathematical operations
b) Recognizing patterns in complex data like images and text
c) Performing logical deductions and reasoning tasks
d) Storing and retrieving large amounts of information
Answer: b) Recognizing patterns in complex data like images and text.
Que 14. Training a neural network often requires:
a) A small set of labeled data samples
b) A significant amount of data and computational resources
c) A specific set of programming instructions
d) A human expert to guide the learning process
Answer: b) A significant amount of data and computational resources.
B. Assertion and reasoning-based questions:
Options:
(a) Both A and R are true and R is the correct explanation for A
(b) Both A and R are true and R is not the correct explanation for A
(c) A is True but R is False
(d) A is false but R is True
Que 1. Assertion (A): Unsupervised Learning is a type of learning without any guidance.
Reasoning (R): Unsupervised learning models work on unlabeled datasets, where the data fed into the machine is random and the person training the model may not have any prior information about it.
Answer: a) Both A and R are true and R is the correct explanation for A.
Que 2. Assertion (A): Information processing in a neural network relies on weights and biases assigned to nodes.
Reasoning (R): These weights and biases determine how strongly a node is influenced by its inputs and its overall contribution to the next layer.
Answer: a) Both A and R are true and R is the correct explanation for A.
C. Answer the following questions.
Que 1. Give a difference between rule based and learning based AI models.
Answer: The difference between rule based and learning based AI models are –
| Rule Based AI Models | Learning Based AI Models |
| A Rule based approach is generally based on the data and rules fed to the machine, where the machine reacts accordingly to deliver the desired output. | Under a Learning based approach, the machine is fed with data and the desired output to which the machine designs its own algorithm (or set of rules) to match the data to the desired output fed into the machine. |
| Rule Based Approach refers to the AI modelling where the relationship or patterns in data are defined by the developer. | A learning-based approach is a method where a computer learns how to do something by looking at examples or getting feedback, similar to how we learn from experience. |
| A rule-based approach is a decision-making or problem-solving method that uses a predefined set of rules, often in the form of “IF-THEN” statements, to arrive at a conclusion or action. | Learning based approach refers to AI modelling where the machine learns by itself. |
Que 2. What is supervised, unsupervised and reinforcement learning? Explain with examples.
Answer: Supervised Learning
In a supervised learning model, the dataset which is fed to the machine is labelled. In other words, we can say that the dataset is known to the person who is training the machine only then he/she is able to label the data.
Example: Used car price prediction, Predicting temperature
Unsupervised Learning
An unsupervised learning model works on an unlabelled dataset. Unsupervised Learning is a type of learning without any guidance. The unsupervised learning models are used to identify relationships, patterns and trends out of the data which is fed into it. Here, the machine is responsible to discover patterns, similarities, and differences on its own based on the unlabeled dataset.
Example: Classifying shapes, Learning of Swimming techniques by child
Reinforcement Learning
- This learning approach enables the computer to make a series of decisions that maximize a reward metric for the task without human intervention and without being explicitly programmed to achieve the task.
- Reinforcement learning is a type of learning in which a machine learns to perform a task through a repeated trial-and-error method.
Example: Parking a Car, Humanoid Walking
Que 3. What is clustering and how is it different from classification?
Answer: Clustering is a process of dividing the data points into different groups or clusters based on their similarity between them.
Clustering vs Classification
| Classification (Supervised Learning) | Clustering (Unsupervised Learning) |
| Classification uses predefined classes in which objects are assigned. Classification is supervised learning. | Clustering finds similarities between objects and places them in the same cluster and it differentiates them from objects in other clusters. Clustering is unsupervised learning. |
Que 4. Explain neural networks. Also give functions of three layers of neural networks.
Answer: Neural networks are modelled on the human brain and nervous system. They are able to automatically extract features without input from the programmer. A neural network is essentially a system of organizing machine learning algorithms to perform certain tasks. It is a fast and efficient way to solve problems for which the dataset is very large, such as in images.
It has three layers – Input Layer, Hidden Layer and Output Layer
- Input Layer: The first layer of a Neural Network is known as the input layer. The job of an input layer is to acquire data and feed it to the Neural Network. No processing occurs at the input layer.
- Hidden Layer: Hidden layers are the layers in which the whole processing occurs. Their name essentially means that these layers are hidden and are not visible to the user.
- Output Layer: The last hidden layer passes the final processed data to the output layer which then gives it to the user as the final output.
Que 5. Differentiate between classification and regression model.
Answer: Classification vs Regression Model
| Classification | Regression |
| This model works on a discrete dataset which means the data need not be continuous. | Regression models work on continuous data. |
| In classification, the data is classified according to the labels. | Regression algorithms predict a continuous value based on the input variables. |
| Example: Classifying weather is Hot or Cold | Example: Predicting Temperature |
Que 6. What is a neural network? Give the functioning of its three layers?
Answer: Same as Que 4
Que 7. Identify the type of learning (supervised, unsupervised, reinforcement learning) are the following case studies most likely based on?
a) Case Study 1:
A company wants to predict customer churn based on past purchasing behavior, demographics, and customer interactions. They have a dataset with labeled examples of customers who churned and those who did not.
Answer: Supervised Learning
b) Case Study 2:
A social media platform wants to group users based on their interests and behavior to recommend relevant content. They have a large dataset of user interactions but no predefined categories. Which type of learning is this case study most likely based on?
Answer: Unsupervised Learning
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 case study most likely based on?
Answer: Reinforcement Learning
d) Case Study 4:
A healthcare provider wants to identify patterns in patient data to personalize treatment plans. They have a dataset with various patient attributes but no predefined labels indicating specific treatment plans. Which type of learning is this case study most likely based on?
Answer: Unsupervised Learning
e) Case Study 5:
A manufacturing company wants to optimize its production process by detecting anomalies in sensor data from machinery. They have a dataset with examples of normal and anomalous behavior. Which type of learning is this case study most likely based on?
Answer: Supervised Learning
Q8. Identify the type of model (classification, regression, clustering, association model) are the following case studies most likely based on?
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.
Answer: Classification
b) A real estate agency wants to predict the selling price of houses based on various features such as size, location, number of bedrooms, and bathrooms. They have a dataset containing historical sales data.
Answer: Regression
c) A marketing company wants to segment its customer base into distinct groups based on purchasing behavior for targeted marketing campaigns. They have a dataset containing information such as purchase history, frequency of purchases, and amount spent.
Answer: Clustering
d) A grocery store wants to identify associations between different products purchased by customers to understand which products are commonly bought together. They have a transaction dataset containing records of items purchased together during each transaction.
Answer: Association
Que 9. A healthcare provider wants to improve patient care by predicting the length of hospital stays for different medical conditions. They have a dataset containing patient demographics, medical history, and treatment details. The task involves:
a) To predict whether a patient will have a short or long hospital stay.
b) To predict the number of days a patient will stay in the hospital.
c) To segment patients into groups with similar characteristics for personalized treatment plans.
d) To identify patterns in patient treatments and outcomes.
Identify the type of model (classification, regression, clustering, and association model) in the above tasks.
Answer: (a) Classification
(b) Regression
(c) Clustering
(d) Association
Que 10. Convert the following scenarios to a perceptron:
a) Context: A manager is deciding whether to approve a work-from-home request from an employee.
Factors:
– Does the employee perform well when working remotely?
– Are there any upcoming team meetings or collaborative projects?
– Does the company’s policy support remote work?
– Is it beneficial for both the employee and the company?
Answer: Perceptron Model for Work-From-Home Approval
- Inputs (Binary):
- x₁ = Employee performs well remotely
- x₂ = No upcoming team meetings
- x₃ = Company policy supports remote work
- x₄ = Beneficial for employee and company
- Weights:
w₁ = w₂ = w₃ = w₄ = 1 - Bias:
b = −3
Perceptron Equation:
y = 1 if (x1 + x2 + x3 + x4 − 3) ≥ 0
y = 0 otherwise
Output:
y = 1 → Approve work-from-home
y = 0 → Reject work-from-home
b) Context: A homeowner is deciding whether to invest in solar panels for their house.
Factors:
– Do I have a sufficient average amount of sunlight in my area?
– Are there any available incentives or rebates for installing solar panels?
– Does installing solar panels impact the value of my home?
– Does solar energy lead to environmental benefits?
Answer: Perceptron Model for Installing Solar Panels (Exam-ready answer):
Inputs (Binary):
x₁ = Sufficient sunlight available
x₂ = Incentives or rebates available
x₃ = Increases home value
x₄ = Environmental benefits
Weights: w₁ = w₂ = w₃ = w₄ = 1
Bias: b = −3
Perceptron Equation:
y = 1 if (x1 + x2 + x3 + x4 − 3) ≥ 0
y = 0 otherwiseOutput:
y = 1 → Install solar panels
y = 0 → Do not install solar panels
By Anjeev Kr Singh – Computer Science Educator
Published on : January 6, 2026 | Updated on : January 6, 2026
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