Unit 5 - 16 mark quest

                  

Unit 5

16 mark Questions

1. Explain in detail about Learning with Decision Tree?

2. Write about Explanation Based Learning?

3. Explain about Statistical Learning?

4. Explain in detail about Reinforcement Learning? 

5. a)    Give a brief overview on decision tree inductive learning algorithm. (16) 

6. Discuss the following:

       (i)     Passive reinforcement learning. (8)

       (ii)    Active reinforcement learning. (8)

Unit 5 - 2 mark quest

Unit 5

2 mark questions.

1. Define Learning Agent.
2. List out the 3 types of Machine Learning.
3. Define Supervised Learning.
4. Define UnSupervised Learning.
5. Define reinforcement Learning.
6. Define Decision tree
7. Define training Set
8. What is information gain.
9. Define test set.
10. What is Overfitting.
11. What is the solution for overfitting.
12. What memoization.
13. Define Bayesian learning.
14. Define MAP.
15.Define MDL.
16. What is Maximum likelihood
17.What is reward.
18.Define MDP.
19. Define Passive Learning.
20. Define Active Learning.

Unit 4 -16 mark questions.

Unit 4

 16 mark questions

1. Write in detail of basic probability.
2. Explain in detail about Bayesian Network.
3. Explain about inference in Bayesian Network & their types.
4.What is Temporal Model write in detail.
5. Explain about Hidden Markov Model.

Unit 4 - 2 mark quest

Unit 4

2 Mark Questions

 1. Define Uncertainty
 2. List out the 3 reasons why medical diagnosis fails to use FOL
 3. Define Conditional Probability & its other name.
 4. Define UnConditional Probability & its other name.
 5. Define Decision theory
 6. What are the 3 types  of random variables
 7. State the Product rule.
8. What is Kolmogorov's axioms.
9. Give an example for bayesian network
10. What is CPT.
11. Define Markov Assumption.
12. Define transition Model.
13. Define Sensor Model.
14. What are the two types of Markov Process.
15. What are the basic inference tasks.
16. Define Hidden Markov Model.