Machine Learning Techniques PYQ 2024-25 AKTU (BCDS062)

AKTU · BTECH · Semester 5, 6 · Machine Learning Techniques · Session 2024-25 · PYQ

AKTU Machine Learning Techniques (BCDS062) previous year question paper 2024-25 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 2024-25

  1. Q1a. Differentiate between supervised and unsupervised learning. (2 marks, 2024-25)
  2. Q1b. What is the role of the hypothesis in a learning system? (2 marks, 2024-25)
  3. Q1c. Define hyper plane in the context of SVM. (2 marks, 2024-25)
  4. Q1d. Differentiate between linear and logistic regression. (2 marks, 2024-25)
  5. Q1e. What is inductive bias? (2 marks, 2024-25)
  6. Q1f. What is gradient descent? (2 marks, 2024-25)
  7. Q1g. Define the Markov Decision Process (MDP). (2 marks, 2024-25)
  8. Q2a. Compare Naïve Bayes classifier and Bayesian belief networks. (7 marks, 2024-25)
  9. Q2b. Discuss the properties that make SVM effective for high-dimensional data with example. (7 marks, 2024-25)
  10. Q2c. Explain how k-NN works. What are its advantages and limitations? (7 marks, 2024-25)
  11. Q2d. Describe the basic architecture of a convolution neural network (CNN) with help of diagram. (7 marks, 2024-25)
  12. Q2e. Explain the GA cycle of reproduction with a neat diagram. (7 marks, 2024-25)
  13. Q3a. How Machine Learning algorithms help in detecting fraudulent activities in finance sector? Also give some real-time software used. (7 marks, 2024-25)
  14. Q3b. Compare regression, classification and clustering in machine learning along with suitable real-life applications. (7 marks, 2024-25)
  15. Q4a. Discuss entropy and information gain in detail. Why are they important in decision tree learning? Illustrate with a numerical example. (7 marks, 2024-25)
  16. Q4b. Discuss the concept of Radial Basis Function (RBF) networks. Provide the mathematical formulation and architecture. (7 marks, 2024-25)
  17. Q5a. Explain the working of instance-based learning. Use diagrams to show how classification is done using k-NN. (7 marks, 2024-25)
  18. Q5b. Explain the concept of SVM kernels. Compare different SVM kernels with mathematical formulation and application suitability. Also, explain how SVM determines the optimal hyper plane. (7 marks, 2024-25)
  19. Q6a. Explain the architecture and functioning of a Multilayer Perception (MLP). How does it differ from a single-layer perceptron? (7 marks, 2024-25)
  20. Q6b. Discuss the concept of CNN architecture. Explain the role of different layers such as convolution, pooling, activation, and fully connected layers. (7 marks, 2024-25)
  21. Q7a. What is Deep Q-learning? How does it improve over traditional Q-learning? Explain with an architecture diagram. (7 marks, 2024-25)
  22. Q7b. Compare Genetic Algorithms and Reinforcement Learning in terms of learning paradigm, memory, and exploration strategy. (7 marks, 2024-25)

AKTU paper codes: BCS055, KCS055, BCDS062, KAI601

Machine Learning Techniques previous year papers

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