Class 10 Computer Science — Chapter 6: Introduction to Artificial Intelligence (AI) and Machine Learning (ML)

Board: Punjab Board

Exercise Solutions

Multiple Choice Questions (MCQs)

1. What is Artificial Intelligence (AI)?

(a) Solving math problems by hand
(b) Machines performing tasks like humans
(c) Sending emails using a computer
(d) Watching videos online

Correct Answer: (b) Machines performing tasks like humans

2. Which of the following is an example of AI?

(a) A chatbot replying to your questions
(b) Typing on a keyboard
(c) A TV remote
(d) A machine without internet

Correct Answer: (a) A chatbot replying to your questions

3. What is Machine Learning (ML)?

(a) Teaching humans to use machines
(b) Teaching machines to learn from data like humans
(c) Learning computer hardware basics
(d) A machine without internet

Correct Answer: (b) Teaching machines to learn from data like humans

4. Which app recommends videos using ML?

(a) Google Maps
(b) Microsoft Paint
(c) YouTube
(d) Windows Media Player

Correct Answer: (c) YouTube

5. In supervised learning, the data is:

(a) Unorganized
(b) Without any answers
(c) Labeled with correct answers
(d) Only text-based

Correct Answer: (c) Labeled with correct answers

6. Which of the following is an example of unsupervised learning?

(a) Predicting student marks
(b) Identifying spam emails
(c) Marking quizzes
(d) Grouping students by interest without labels

Correct Answer: (d) Grouping students by interest without labels

7. Google Assistant is an example of:

(a) A game app
(b) A word processor
(c) A smart assistant using AI
(d) A calculator

Correct Answer: (c) A smart assistant using AI

8. What skill is essential for a data analyst?

(a) Painting
(b) Basic drawing
(c) Interpreting and visualizing data
(d) Flying drones

Correct Answer: (c) Interpreting and visualizing data

9. AI is mostly used for:

(a) Handwriting notes
(b) Making phone calls
(c) Charging phones
(d) Smart decision-making and automation

Correct Answer: (d) Smart decision-making and automation

10. A chatbot on JazzCash is an example of:

(a) Cloud computing
(b) Data visualization
(c) AI in business
(d) Unsupervised learning

Correct Answer: (c) AI in business

Short Answer Questions

1. What is the difference between AI and ML?

Answer:

Artificial Intelligence (AI): A broad field of computer science focused on creating intelligent machines capable of simulating human intelligence, decision-making, and problem-solving.
Machine Learning (ML): A subset of AI in which algorithms learn patterns from data to make predictions or decisions without being explicitly programmed for every task.

2. Give one real-life example of AI used in Pakistan.

Answer:

An automated AI customer service chatbot on JazzCash or Easypaisa is an example of AI used in Pakistan. It can handle customer inquiries and guide users through financial services automatically.

3. How does supervised learning work?

Answer:

Supervised learning trains a model using a labeled dataset containing input features and their corresponding correct answers (labels). The algorithm learns the relationship between inputs and outputs so it can predict results for new, unseen data.

4. Define a confusion matrix.

Answer:

A confusion matrix is a table used to evaluate the performance of a machine learning classification model. It compares actual values with predicted values across four categories:

True Positive (TP): A positive case correctly predicted as positive.
True Negative (TN): A negative case correctly predicted as negative.
False Positive (FP): A negative case incorrectly predicted as positive.
False Negative (FN): A positive case incorrectly predicted as negative.

5. How can businesses use AI?

Answer:

Businesses use AI in several ways:

Automated Customer Support: AI chatbots answer frequently asked questions around the clock.
Personalized Marketing and Recommendations: AI suggests products based on customers’ browsing histories and preferences.
Fraud Detection: AI monitors financial transactions to identify potentially fraudulent activities.

6. Give one example each of a smart assistant and a recommendation system.

Answer:

Smart Assistant: Google Assistant or Apple Siri.
Recommendation System: YouTube’s video recommendation system or Netflix’s movie recommendation system.

7. What kind of data is used in unsupervised learning?

Answer:

Unsupervised learning uses unlabeled data, which does not contain predefined answers or target categories. The algorithm identifies hidden patterns, structures, or natural groupings within the dataset.

Long Answer Questions

1. Explain supervised and unsupervised learning with suitable examples.

Answer:

Supervised Learning

Definition: A machine learning approach in which an algorithm is trained using a labeled dataset containing input features and correct target outputs.
Mechanism: The model learns the relationship between inputs and outputs and adjusts its parameters to reduce prediction errors.
Example — Email Spam Detection: The model is trained on emails labeled as “Spam” or “Not Spam.” After training, it classifies incoming emails automatically.

Unsupervised Learning

Definition: A machine learning approach in which an algorithm processes unlabeled data to discover hidden patterns, relationships, or natural groupings.
Mechanism: The model analyzes similarities, distributions, and distances between data points without predefined answers.
Example — Customer Segmentation: An e-commerce business groups customers according to shopping frequency and spending habits without assigning predefined labels.

2. Explain how model performance is evaluated in machine learning using the following metrics:

(a) Confusion Matrix
(b) Accuracy
(c) Precision

Answer:

(a) Confusion Matrix

A confusion matrix is a \(2 \times 2\) table used to evaluate a classification model. It summarizes the model’s predictions using four categories:

True Positive (TP): Correctly predicted positive cases.
True Negative (TN): Correctly predicted negative cases.
False Positive (FP): Negative cases incorrectly predicted as positive (Type I error).
False Negative (FN): Positive cases incorrectly predicted as negative (Type II error).

(b) Accuracy

Accuracy measures the proportion of all predictions that are correct.

Formula:

\[ \text{Accuracy}=\frac{TP+TN}{TP+TN+FP+FN} \]

Precision measures how many of the cases predicted as positive are actually positive.

Formula:

\[ \text{Precision}=\frac{TP}{TP+FP} \]

3. A model predicts whether students will pass (1) or fail (0). Out of 20 students:

True Positives (TP) = 8
True Negatives (TN) = 6
False Positives (FP) = 4
False Negatives (FN) = 2

You are required to:

(a) Fill in the confusion matrix.
(b) Calculate accuracy.

Answer:

(a) Filled Confusion Matrix

Actual Class	Predicted: Pass (1)	Predicted: Fail (0)
Actual: Pass (1)	TP = 8	FN = 2
Actual: Fail (0)	FP = 4	TN = 6

(b) Accuracy Calculation

Formula:

\[ \text{Accuracy}=\frac{TP+TN}{TP+TN+FP+FN} \]

Substitution:

\[ \text{Accuracy}=\frac{8+6}{8+6+4+2} \]

\[ =\frac{14}{20} \]

\[ =0.70 \]

Final Result:

\[ \boxed{\text{Accuracy}=70\%} \]

The model’s accuracy is 70%, meaning it correctly predicted the outcomes for 14 out of 20 students.

Notes by Sir Dilawar

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