Machine Learning Techniques PYQ 2025-26 AKTU (BCDS062)

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

AKTU Machine Learning Techniques (BCDS062) 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 and list its different types. (2 marks, 2025-26)
  2. Q1b. Explain the concept of well-defined learning problem with suitable example. (2 marks, 2025-26)
  3. Q1c. State Bayes Theorem. (2 marks, 2025-26)
  4. Q1d. Explain the concept of overfitting in Decision Trees? (2 marks, 2025-26)
  5. Q1e. Define k-Nearest Neighbor algorithm. (2 marks, 2025-26)
  6. Q1f. Define activation function in neural networks. (2 marks, 2025-26)
  7. Q1g. Define Reinforcement Learning with one example. (2 marks, 2025-26)
  8. Q2a. Explain the design of a learning system with a suitable example. (7 marks, 2025-26)
  9. Q2b. Describe Linear Regression and Logistic Regression with differences. (7 marks, 2025-26)
  10. Q2c. Explain the working of Naïve Bayes Classifier with assumptions. (7 marks, 2025-26)
  11. Q2d. Discuss Information Gain and its role in Decision Tree Learning. (7 marks, 2025-26)
  12. Q2e. Explain types of kernels used in Support Vector Machines. (7 marks, 2025-26)
  13. Q3a. Explain various Machine Learning approaches such as ANN, Clustering, and Reinforcement Learning. (7 marks, 2025-26)
  14. Q3b. Discuss issues in Machine Learning and differentiate between Data Science and Machine Learning. (7 marks, 2025-26)
  15. Q4a. Explain Bayes Optimal Classifier and Bayesian Belief Networks. (7 marks, 2025-26)
  16. Q4b. Describe the EM Algorithm with suitable example. (7 marks, 2025-26)
  17. Q5a. Explain ID3 Algorithm with an example. (7 marks, 2025-26)
  18. Q5b. Discuss k-Nearest Neighbor and Locally Weighted Regression. (7 marks, 2025-26)
  19. Q6a. Derive the Backpropagation Algorithm for Multilayer Neural Networks. (7 marks, 2025-26)
  20. Q6b. Explain CNN architecture and different types of layers. (7 marks, 2025-26)
  21. Q7a. Explain Q-Learning algorithm and Markov Decision Process. (7 marks, 2025-26)
  22. Q7b. Describe Genetic Algorithm cycle including crossover and mutation. (7 marks, 2025-26)

AKTU paper codes: BCS055, KCS055, BCDS062, KAI601

Machine Learning Techniques previous year papers

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