India of My Dreams
Artificial Intelligence question bank
Unit 4 –Machine learning
Machine Learning : Supervised and unsupervised learning, Decision trees, Statistical learning models, Learning with complete data - Naive Bayes models, Learning with hidden data - EM algorithm, Reinforcement learning 1. What is learning? 2. Explain a General model of Learning Agent? 3. Why do we require Machine Learning ? 4. What are the paradigms of Machine Learning ? 5. Application of Machine Learning ? (Data Mining, Knowledge Discovery, DSS E.TC) 6. What are the types of Learning ?(supervised, unsupervised, reinforcement learning) 7. Difference between Supervised and UnSupervised Learning 8. What is Inductive Learning? What are the issues in inductive Learning 9. What is Decision Tree ? .How DT is used in Inductive Learning 10. Write Short note on Explanation based learning? 11. Define the term “reinforcement learning? How does passive “ reinforcement learning” differ from active “reinforcement learning”. 12. Explain the statistical nature of Learning Process 13. Explain (Maximum a posteriori) and Maximum likelihood hypothesis 14. What do u understand by learning with complete data 15. Illustrate Naïve Bayes Model of statistical learning 16. Write short notes on :- a) Naïve Bayes Model b) EM algorithm c) Statistical Learning Model 17. Describe different unsupervised learning 18. What is clustering? Describe K-Mean clustering 19. What is learning with complete data. Give Examples. 20. What is learning by analogy 21. What is Learning with hidden data . Give Examples. 22. Difference between Learning with complete and learning with hidden data.
Unit 5
Pattern Recognition : Introduction, Design principles of pattern recognition system, Statistical
Pattern recognition, Parameter estimation methods - Principle Component Analysis (PCA) and
Linear Discriminant Analysis (LDA), Classification
Unit 4 –Machine learning
Machine Learning : Supervised and unsupervised learning, Decision trees, Statistical learning models, Learning with complete data - Naive Bayes models, Learning with hidden data - EM algorithm, Reinforcement learning 1. What is learning? 2. Explain a General model of Learning Agent? 3. Why do we require Machine Learning ? 4. What are the paradigms of Machine Learning ? 5. Application of Machine Learning ? (Data Mining, Knowledge Discovery, DSS E.TC) 6. What are the types of Learning ?(supervised, unsupervised, reinforcement learning) 7. Difference between Supervised and UnSupervised Learning 8. What is Inductive Learning? What are the issues in inductive Learning 9. What is Decision Tree ? .How DT is used in Inductive Learning 10. Write Short note on Explanation based learning? 11. Define the term “reinforcement learning? How does passive “ reinforcement learning” differ from active “reinforcement learning”. 12. Explain the statistical nature of Learning Process 13. Explain (Maximum a posteriori) and Maximum likelihood hypothesis 14. What do u understand by learning with complete data 15. Illustrate Naïve Bayes Model of statistical learning 16. Write short notes on :- a) Naïve Bayes Model b) EM algorithm c) Statistical Learning Model 17. Describe different unsupervised learning 18. What is clustering? Describe K-Mean clustering 19. What is learning with complete data. Give Examples. 20. What is learning by analogy 21. What is Learning with hidden data . Give Examples. 22. Difference between Learning with complete and learning with hidden data.
Unit 5
Pattern Recognition : Introduction, Design principles of pattern recognition system, Statistical
Pattern recognition, Parameter estimation methods - Principle Component Analysis (PCA) and
Linear Discriminant Analysis (LDA), Classification