Machine Learning Techniques PYQ 2025-26 AKTU (BCS055)

AKTU · BTECH · Semester 5, 6 · Machine Learning Techniques · Session 2025-26 · PYQ

AKTU Machine Learning Techniques (BCS055) previous year question paper 2025-26 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 2025-26

  1. Q1a. Define Machine Learning. (2 marks, 2025-26)
  2. Q1b. What is a well-defined learning problem? (2 marks, 2025-26)
  3. Q1c. State any two applications of Logistic Regression. (2 marks, 2025-26)
  4. Q1d. What is Entropy in Decision Tree Learning? (2 marks, 2025-26)
  5. Q1e. Define Support Vector Machine (SVM). (2 marks, 2025-26)
  6. Q1f. What is backpropagation in neural networks? (2 marks, 2025-26)
  7. Q1g. What is Q-learning? (2 marks, 2025-26)
  8. Q2a. Explain types of learning in Machine Learning with suitable examples. (7 marks, 2025-26)
  9. Q2b. Describe Linear Regression and its assumptions. (7 marks, 2025-26)
  10. Q2c. Explain Naïve Bayes classifier with an example. (7 marks, 2025-26)
  11. Q2d. Discuss the properties and issues of Support Vector Machines. (7 marks, 2025-26)
  12. Q2e. Explain k-Nearest Neighbour (k-NN) algorithm and its limitations. (7 marks, 2025-26)
  13. Q3a. Explain the ID3 algorithm for Decision Tree learning. (7 marks, 2025-26)
  14. Q3b. Discuss inductive bias and inductive inference in decision trees. (7 marks, 2025-26)
  15. Q4a. Explain perceptron and multilayer perceptron with neat diagram. (7 marks, 2025-26)
  16. Q4b. Derive the Backpropagation algorithm for training neural networks. (7 marks, 2025-26)
  17. Q5a. Explain the architecture of Convolutional Neural Networks (CNN). (7 marks, 2025-26)
  18. Q5b. Discuss applications of Deep Learning with suitable case studies. (7 marks, 2025-26)
  19. Q6a. Explain Reinforcement Learning with Markov Decision Process. (7 marks, 2025-26)
  20. Q6b. Describe Q-Learning algorithm and its applications. (7 marks, 2025-26)
  21. Q7a. Explain Genetic Algorithm cycle with suitable example. (7 marks, 2025-26)
  22. Q7b. Analyze the role of crossover and mutation in Genetic Algorithms. (7 marks, 2025-26)

AKTU paper codes: BCS055, KCS055, BCDS062, KAI601

Machine Learning Techniques previous year papers

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