Machine Learning Techniques PYQ 2022-23 AKTU (KAI601)

AKTU · BTECH · Semester 5, 6 · Machine Learning Techniques · Session 2022-23 · PYQ

AKTU Machine Learning Techniques (KAI601) previous year question paper 2022-23 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 2022-23

  1. Q1a. Define classification tree and regression tree. (2 marks, 2022-23)
  2. Q1b. Discuss reason of opting unsupervised learning over supervised learning. (2 marks, 2022-23)
  3. Q1c. Discuss the features of learning problems. (2 marks, 2022-23)
  4. Q1d. Discuss the issues in machine learning. (2 marks, 2022-23)
  5. Q1e. List the issues in Decision tree learning. (2 marks, 2022-23)
  6. Q1f. Discuss the characteristics of the problems suited for decision tree learning. (2 marks, 2022-23)
  7. Q1g. Write different applications of neural networks (NN). (2 marks, 2022-23)
  8. Q1h. Define Delta Rule. (2 marks, 2022-23)
  9. Q1i. What are the types of neuron connection? (2 marks, 2022-23)
  10. Q1j. Discuss the perspective and issues in machine learning. (2 marks, 2022-23)
  11. Q2a. Discuss Inductive Bias in Decision Tree Learning. Differentiate between two types of biases. Discuss the reason to prefer short hypotheses? (10 marks, 2022-23)
  12. Q2b. Write the algorithm for back propagation. (10 marks, 2022-23)
  13. Q2c. Write a detail note on naive bayes linear models. (10 marks, 2022-23)
  14. Q2d. Discuss the types of problems in which Artificial Neural Network can be applied. (10 marks, 2022-23)
  15. Q2e. Explain in detail the concept of evaluating hypotheses. (10 marks, 2022-23)
  16. Q3a. Describe the reinforcement learning in detail with suitable examples. (10 marks, 2022-23)
  17. Q3b. Discuss the learning. Write any four learning techniques and in each case give the expression for weight- updating. (10 marks, 2022-23)
  18. Q4a. Explain the process to find complexity for finite hypothesis spaces and Complexity for infinite hypothesis spaces. (10 marks, 2022-23)
  19. Q4b. Discuss the neighbors? Also discuss the reason of necessary use of nearest neighbor while classifying justify the answer with suitable example. (10 marks, 2022-23)
  20. Q5a. Discuss Bayesian learning and its impacts in machine learning? (10 marks, 2022-23)
  21. Q5b. Write a short note on following: i. Hypothesis space search ii. General-to-specific ordering of hypotheses (10 marks, 2022-23)
  22. Q6a. Explain that genetic algorithms are influenced by knowledge based techniques. Also discuss the how genetic algorithm is different from traditional algorithms. (10 marks, 2022-23)
  23. Q6b. Write a short note on the following: i. EM algorithm ii. Generalization (10 marks, 2022-23)
  24. Q7a. Explain Find -S Algorithm in detail. Also discuss the properties and complaints of Find S. (10 marks, 2022-23)
  25. Q7b. Explain the inductive biased hypothesis space, unbiased learner and the futility of bias free learning. Describe the three types of learner. (10 marks, 2022-23)

AKTU paper codes: BCS055, KCS055, BCDS062, KAI601

Machine Learning Techniques previous year papers

More Machine Learning Techniques resources

Browse all notes · Semester 5 notes