Machine Learning Techniques PYQ 2022-23 AKTU (KCS055)

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

AKTU Machine Learning Techniques (KCS055) previous year question paper 2022-23 for B.Tech Semester 5/6. Covers Introduction, Regression, Bayesian Learning…

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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. Discuss model representation of artificial neuron. (2 marks, 2022-23)
  2. Q1b. Explain general to specific ordering hypothesis in concept learning. (2 marks, 2022-23)
  3. Q1c. Discuss support vectors in SVM. (2 marks, 2022-23)
  4. Q1d. Compare Artificial Intelligence and Machine Learning. (2 marks, 2022-23)
  5. Q1e. Discuss reinforcement learning. (2 marks, 2022-23)
  6. Q1f. Illustrate the advantages of instance-based learning techniques over other machine learning techniques. (2 marks, 2022-23)
  7. Q1g. Differentiate between Gradient Descent and Stochastic Gradient Descent. (2 marks, 2022-23)
  8. Q1h. Compare ANN and Bayesian network. (2 marks, 2022-23)
  9. Q1i. Illustrate Markov decision model. (2 marks, 2022-23)
  10. Q1j. Differentiate between Q learning and Deep Learning. (2 marks, 2022-23)
  11. Q2a. Explain supervised and unsupervised learning techniques. (10 marks, 2022-23)
  12. Q2b. Discuss linear regression and logistic regression in detail. (10 marks, 2022-23)
  13. Q2c. Describe the following concepts in decision tree in detail: (i) Avoiding overfitting in decision tree. (ii) Incorporating continuous valued attributes. (10 marks, 2022-23)
  14. Q2d. Explain various types of activation functions with examples. (10 marks, 2022-23)
  15. Q2e. Illustrate the process of Q-learning and discuss the following terms: (i) Q-values or action value (ii) Rewards and Episode (iii) Temporal difference or TD update. (10 marks, 2022-23)
  16. Q3a. Illustrate the various areas in which you can apply machine learning. (10 marks, 2022-23)
  17. Q3b. Compare regression, classification and clustering in machine learning along with suitable real-life applications. (10 marks, 2022-23)
  18. Q4a. Discuss the role of Bayes theorem in machine learning. How naive Bayes algorithm is different from Bayes theorem? (10 marks, 2022-23)
  19. Q4b. Explain hyperplane (decision boundary) in SVM. Categorize various popular kernels associated with SVM. (10 marks, 2022-23)
  20. Q5a. Demonstrate K-Nearest Neighbors algorithm for classification with the help of an example. (10 marks, 2022-23)
  21. Q5b. Explain Instance based learning. Compare locally weighted regression and radial basis function networks. (10 marks, 2022-23)
  22. Q6a. Explain the different layers used in convolutional neural network with suitable examples. (10 marks, 2022-23)
  23. Q6b. Illustrate backpropagation algorithm by assuming the training rules for output unit weights and Hidden Unit weights. (10 marks, 2022-23)
  24. Q7a. Explain various types of reinforcement learning techniques with suitable examples. (10 marks, 2022-23)
  25. Q7b. How to Identify the reproduction cycle of genetic algorithm? Explain with suitable example. (10 marks, 2022-23)

AKTU paper codes: BCS055, KCS055, BCDS062, KAI601

Machine Learning Techniques previous year papers

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