Machine Learning2- UNIT-3 Questions
UNIT-3
Short Answer Questions
1. Define Bayes theorem.
2. Define terms:
a) Prior Probability
b) Conditional Probability
3. Posterior Probability
4. MAP
5. Explain Eager Learning
6. Explain Lazy Learning with an example.
Long Answer Questions
1. Define terms:
a) Prior Probability
b) Conditional Probability
2. Explain EM Algorithm with neat diagram.
3. Explain Brute force Bayes Concept Learning
4. Explain Naïve Bayes Classifier with an Example.
5. Define (i) Prior Probability (ii) Conditional Probability (iii) Posterior Probability
6. Explain the concept of Bayes theorem with an example.
7. State Bayes theorem. Illustrate Bayes theorem with an example.
8. Design the Brute Force Bayesian concept learning algorithm and elaborate.
9. Explain about Bayes theorem.
10. Discuss about Bayesian belief networks.
11. State Bayes theorem. Illustrate Bayes theorem with an example
12. Explain K-Nearest Neighbor Algorithm. List the Advantages and disadvantages of KNN.
13. Outline Locally Weighted Linear Regression.
14. Explain Instance Based Learning. List the remarks on Lazy and Eager Learning.
15. Illustrate K-Nearest Neighbor Algorithm with an example.
16. Describe briefly about k-nearest neighbor algorithm.
17. Write the differences between Eager Learning and Lazy Learning approaches
18. Elaborate the Locally Weighted Linear Regression.
19. Explain Naïve Bayes Classifier. Find out target value for the following instance using below dataset.
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