Unit : Machine Learning

Worksheet - 1

Multiple Choice Questions (MCQs)

1. Which of the following is a type of supervised learning?

• a) Clustering
• b) Regression
• c) Reinforcement Learning
• d) Anomaly Detection

2. What is the primary goal of supervised learning?

• a) To group data points into clusters
• b) To predict outputs based on labeled data
• c) To learn through trial and error
• d) To identify outliers in datasets

3. Which algorithm is typically used for classification tasks in supervised learning?

• a) K-Means Clustering
• b) Linear Regression
• c) K-Nearest Neighbors (KNN)
• d) Reinforcement Learning

4. What is the primary difference between supervised and unsupervised learning?

• a) Supervised learning uses labeled data while unsupervised learning uses unlabeled data
• b) Unsupervised learning uses labeled data while supervised learning uses unlabeled data
• c) Supervised learning is used for clustering tasks
• d) Unsupervised learning predicts specific outputs

5. Which of the following algorithms is used in unsupervised learning?

• a) Decision Trees
• b) K-Means Clustering
• c) Linear Regression
• d) Logistic Regression

6. In reinforcement learning, what mechanism guides the machine toward the optimal action?

• a) Predefined labels
• b) Data clusters
• c) Rewards and penalties
• d) Predicted outcomes

7. Which of the following is an application of supervised learning?

• a) Customer segmentation
• b) Spam filtering
• c) Anomaly detection
• d) Facial recognition

8. What does the "K" in K-Means Clustering represent?

• a) The number of features
• b) The number of clusters
• c) The distance metric
• d) The number of data points

Short-Answer Questions

1. Define the concept of "supervised learning" and give one example.
2. How does K-Means Clustering work in unsupervised learning? Briefly explain the steps.
3. What is the key difference between classification and regression in supervised learning?
4. Give an example of a real-world application of reinforcement learning.

Long-Answer Questions

1. Compare and contrast supervised, unsupervised, and reinforcement learning.
   Explain how each learning type works, their key features, and provide real-world applications for each.
2. Explain the concept of linear regression.
   Include in your explanation the importance of correlation in regression analysis, how the line of best fit is determined, and provide an example where linear regression might be applied in a real-world scenario.