Unit-2: Advanced Concepts of Modeling in AI
Choose the Most Appropriate Answer
1. In which type of machine learning is the data labeled with the desired output?
a) Supervised Learning
b) Unsupervised Learning
c) Reinforcement Learning
d) Deep Learning
✅ Answer: a) Supervised Learning
In supervised learning, data is labeled, meaning both input and correct output are provided to train the model.
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
The spam filter learns from labeled examples of “spam” and “not spam.”
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
Unsupervised learning is used to find patterns or groups in unlabeled data.
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
Reinforcement learning trains an agent by giving rewards for correct actions and penalties for mistakes.
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.
Supervised learning can handle large datasets efficiently with proper algorithms.
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.
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
Clustering is used to group similar items without predefined labels.
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.
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
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
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
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
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
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
Assertion and Reasoning Questions
Q1.
Assertion: Unsupervised Learning is a type of learning without any guidance.
Reasoning: 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.
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
✅ Answer: a) Both A and R are true and R is the correct explanation for A
Q2.
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
Reflection Time
Q1. Give difference between rule-based and learning-based AI models.
✅ Answer:
AI systems work in two ways – by following fixed rules or by learning from experience.
| Rule-Based AI | Learning-Based AI |
| Follows fixed rules made by humans. | Learns automatically from data. |
| Cannot change behavior unless new rules are added. | Keeps improving with new examples. |
| No learning happens. | Machine learns and adapts. |
| Example: Simple chatbot with fixed answers. | Example: Voice assistants like Siri or Alexa. |
Explanation:
Rule-based AI depends on what we program it to do.
Learning-based AI, on the other hand, can study examples and get better with time.
Q2. What is supervised, unsupervised and reinforcement learning? Explain with examples.
✅ Answer:
1. Supervised Learning:
- Works on labeled data where both input and output are known.
- Helps in prediction and classification.
🟩 Example: Predicting house prices from historical data.
2. Unsupervised Learning:
- Works on unlabeled data without predefined answers.
- Finds hidden patterns and groups.
🟩 Example: Grouping students based on performance.
3. Reinforcement Learning:
- Learns by performing actions and receiving rewards or penalties.
- Commonly used in robotics and games.
🟩 Example: Robot learning to walk correctly by trying repeatedly.
Explanation:
Supervised learning is like learning from a teacher.
Unsupervised learning is like discovering things yourself.
Reinforcement learning is like learning from experience.
Q3. What is clustering and how is it different from classification?
✅ Answer:
Clustering is an unsupervised learning technique that groups similar data points based on patterns without using labels.
Classification is a supervised learning technique that assigns data into predefined categories.
| Clustering | Classification |
| Groups data automatically. | Uses labeled data to classify. |
| No predefined output labels. | Known outputs are used for learning. |
| Example: Grouping customers by buying habits. | Example: Classifying emails as “spam” or “not spam.” |
Explanation:
Clustering discovers patterns; classification uses known categories to make predictions.
Q4. Explain neural networks. Also give functions of three layers of neural networks.
✅ Answer:
A Neural Network is a type of AI model inspired by the human brain. It consists of many connected nodes (neurons) that process information in layers.
Three Layers:
- Input Layer: Takes raw data from outside (like numbers, images, or text).
- Hidden Layer: Processes the data and finds patterns using mathematical operations.
- Output Layer: Gives the final result or prediction.
Example:
In handwriting recognition — input layer takes image pixels, hidden layer analyzes strokes, and output layer identifies the letter.
Explanation:
Each layer helps the computer learn step by step, similar to how our brain learns from experience.
Q5. Differentiate between classification and regression model.
✅ Answer:
| Classification Model | Regression Model |
| Used when output is a category (Yes/No, Male/Female). | Used when output is a continuous value (numbers). |
| Output is discrete. | Output is continuous. |
| Example: Predicting whether an email is spam. | Example: Predicting house prices. |
Explanation:
Classification divides data into groups, while regression finds exact numerical outcomes.
Q6. What is neural network? Give the functioning of its three layers.
✅ Answer:
A Neural Network is an AI system that mimics the human brain. It uses neurons, weights, and biases to process data.
Functions of three layers:
- Input Layer: Receives data.
- Hidden Layer: Analyzes and learns from data.
- Output Layer: Produces the final answer.
Example:
Used in face recognition — it takes an image, processes features, and tells whose face it is.
Q7. Identify the type of learning for each case study.
| Case Study | Type of Learning | Explanation |
| a) Predict customer churn (labeled data). | Supervised Learning | Uses past examples with output labels. |
| b) Group users by interests (no labels). | Unsupervised Learning | Finds patterns on its own. |
| c) Self-driving car learns through rewards. | Reinforcement Learning | Learns by feedback and experience. |
| d) Finding patterns in patient data (no labels). | Unsupervised Learning | Works with unlabeled medical data. |
| e) Detect anomalies (labeled examples). | Supervised Learning | Learns from normal and abnormal data. |
Q8. Identify the type of model (classification, regression, clustering, association).
| Case Study | Model Type | Explanation |
| a) Predict loan default or non-default. | Classification Model | Chooses between two categories. |
| b) Predict house price. | Regression Model | Predicts numerical value. |
| c) Segment customers by spending pattern. | Clustering Model | Groups similar customers. |
| d) Find products bought together. | Association Model | Finds relations between products. |
Q9. Identify the model type for hospital stay tasks.
| Task | Model Type | Explanation |
| a) Short or long stay. | Classification Model | Two output categories. |
| b) Number of days. | Regression Model | Continuous numerical prediction. |
| c) Group patients with similar traits. | Clustering Model | Groups by similarity. |
| d) Find common treatment patterns. | Association Model | Finds relationships in data. |