Machine Learning Techniques PYQ 2021-22 AKTU (KCS055)

AKTU · BTECH · Semester 5, 6 · Machine Learning Techniques · Session 2021-22 · PYQ

AKTU Machine Learning Techniques (KCS055) previous year question paper 2021-22 for B.Tech Semester 5/6. Covers Introduction, Regression, Bayesian Learning…

Open the interactive reader to study this resource on AcademicArk.

Machine Learning Techniques AKTU syllabus

  1. Unit 1: Introduction
  2. Unit 2: Regression, Bayesian Learning and Support Vector Machine
  3. Unit 3: Decision Tree Learning and Instance-Based Learning
  4. Unit 4: Artificial Neural Networks and Deep Learning
  5. Unit 5: Reinforcement Learning and Genetic Algorithms

Questions in Machine Learning Techniques AKTU PYQ 2021-22

  1. Q1a. What is a “Well -posed Learning “problem? Explain with an example. (2 marks, 2021-22)
  2. Q1b. What is Occam's razor in ML? (2 marks, 2021-22)
  3. Q1c. What is the role of Inductive Bias in ANN? (2 marks, 2021-22)
  4. Q1d. What is gradient descent delta rule? (2 marks, 2021-22)
  5. Q1e. What is Paired t Tests in Hypothesis evaluation? (2 marks, 2021-22)
  6. Q1f. How do you find the confidence interval for a hypothesis test? (2 marks, 2021-22)
  7. Q1g. What is sample complexity of a Learning Problem? (2 marks, 2021-22)
  8. Q1h. Differentiate between Lazy and Eager Learning (2 marks, 2021-22)
  9. Q1i. What is the problem of crowding in GA (2 marks, 2021-22)
  10. Q1j. Comparison of purely analytical and purely inductive learning. (2 marks, 2021-22)
  11. Q2a. Design the Final design of checkers learning program. (10 marks, 2021-22)
  12. Q2b. What is Maximum Likelihood and Least Squared Error Hypothesis? (10 marks, 2021-22)
  13. Q2c. What problem does the EM algorithm solve (10 marks, 2021-22)
  14. Q2d. Highlight the importance of Case Based Learning (10 marks, 2021-22)
  15. Q2e. Write short notes on Learning First Order Rules (10 marks, 2021-22)
  16. Q3a. Explain the “Concept Learning” Task Giving an example (10 marks, 2021-22)
  17. Q3b. Find the maximally general hypothesis and maximally specific hypothesis for the training examples given in the table using the candidate elimination algorithm. Given Training Example: Sky Temp Humidity wind water Forecast sport Sunny warm Normal Strong warm same Yes Sunny warm High Strong warm… (10 marks, 2021-22)
  18. Q4a. Comment on the Algorithmic convergence & Generalization property of ANN. (10 marks, 2021-22)
  19. Q4b. Discuss the following issues in Decision Tree Learning: 1. Overfitting the data 2. Guarding against bad attribute choices 3. Handling continuous valued attributes 4. Handling missing attribute values 5. Handling attributes with differing costs (10 marks, 2021-22)
  20. Q5a. How is Naïve Bayesian Classifier different from Bayesian Classifier? (10 marks, 2021-22)
  21. Q5b. Explain the role of Central Limit Theorem Approach for deriving Confidence Interval. (10 marks, 2021-22)
  22. Q6a. Write short notes on Probably Approximately Correct (PAC) learning model. (10 marks, 2021-22)
  23. Q6b. Discuss various Mistake Bound Model of Learning (10 marks, 2021-22)
  24. Q7a. What is the significance of Learn -one Rule Algorithm? (10 marks, 2021-22)
  25. Q7b. Describe a prototypical genetic algorithm along with various operations possible in it. (10 marks, 2021-22)

AKTU paper codes: BCS055, KCS055, BCDS062, KAI601

Machine Learning Techniques previous year papers

More Machine Learning Techniques resources

Browse all notes · Semester 5 notes