Machine Learning Techniques PYQ 2024-25 AKTU (BCS055)

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

AKTU Machine Learning Techniques (BCS055) 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. What constitutes a well-defined learning problem in machine learning? Give an example. (2 marks, 2024-25)
  2. Q1b. How does machine learning differ from data science, and how do they complement each other? (2 marks, 2024-25)
  3. Q1c. Explain the role of the sigmoid function in logistic regression. (2 marks, 2024-25)
  4. Q1d. What is the significance of the decision surface in Support Vector Machines (SVM)? (2 marks, 2024-25)
  5. Q1e. What is overfitting in decision tree learning, and how can it be avoided? (2 marks, 2024-25)
  6. Q1f. Explain the role of the activation function in a perceptron. (2 marks, 2024-25)
  7. Q1g. Describe the role of the reward function in reinforcement learning. (2 marks, 2024-25)
  8. Q2a. Describe the steps involved in designing a machine learning system. Illustrate each step with a real-world example. (7 marks, 2024-25)
  9. Q2b. Explore the difference between Simple Linear Regression and Multiple Linear Regression. How does the inclusion of multiple features affect the model? (7 marks, 2024-25)
  10. Q2c. Illustrate the steps of the ID3 algorithm, including how it selects attributes and constructs the tree. (7 marks, 2024-25)
  11. Q2d. Discuss the Self-Organizing Map (SOM) algorithm. How does SOM work for clustering and dimensionality reduction? Explain the differences between SOM and supervised learning models. (7 marks, 2024-25)
  12. Q2e. What is the role of the Q-learning function in reinforcement learning? Explain how it helps the agent learn optimal actions and policies. (7 marks, 2024-25)
  13. Q3a. Discuss the history of machine learning. Highlight major milestones and advancements in the field. (7 marks, 2024-25)
  14. Q3b. Compare and contrast supervised, unsupervised, and reinforcement learning. Provide examples where each type is applicable. (7 marks, 2024-25)
  15. Q4a. Consider the following weather data to determine whether or not a person will play tennis on a given day based on weather conditions: [Table provided] Apply Naïve Bayes Classifier to predict whether the person will play or not play tennis on given condition {Outlook = Sunny, Temperature = Cool,… (7 marks, 2024-25)
  16. Q4b. Fit a linear regression model for the dataset (x, y): (1, 1.5), (2, 3.0), (3, 4.5), (4, 6.0) and predict y for x = 5. (7 marks, 2024-25)
  17. Q5a. Describe the concept of locally weighted regression (LWR). How does it differ from traditional regression models, and what are its practical applications? (7 marks, 2024-25)
  18. Q5b. Given the following dataset with two attributes (A and B), and a target variable (T): [Table provided] Calculate the entropy before the split and after the split on attribute A. Which attribute (A or B) gives the highest information gain? (7 marks, 2024-25)
  19. Q6a. Derive the mathematical steps involved in backpropagation for training a neural network. (7 marks, 2024-25)
  20. Q6b. Explain the architecture and functioning of Convolutional Neural Networks (CNNs). (7 marks, 2024-25)
  21. Q7a. Explain the concept of Reinforcement Learning (RL). Discuss how it differs from supervised and unsupervised learning. (7 marks, 2024-25)
  22. Q7b. Explain the components of a genetic algorithm. Discuss the role of chromosomes, genes, and the population in the GA cycle. (7 marks, 2024-25)

AKTU paper codes: BCS055, KCS055, BCDS062, KAI601

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