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EndsemNov 20252019 patternBE AI&DS

Machine Learning (ML) SPPU Endsem Question Paper Nov 2025

All 8 questions from this SPPU endsem paper, as printed. Read them here or open the PDF.

Open the PDFMost repeated ML questions
Subject code
417521
Pattern
2019 pattern
Exam
Endsem
Session
Nov 2025
Max. marks
70
Time
2½ Hours
Semester
VII
Paper code
[6584]-383

Printed for: B.E. (AIDS)

Instructions to the candidates

  1. Answer Q.1 or Q.2, Q.3 or Q.4, Q.5 or Q.6 and Q.7 or Q.8.
  2. Neat diagrams must be drawn wherever necessary.
  3. Figures to the right indicate full marks.
  4. Use of electronic pocket calculator is allowed.

Questions

  1. Q1

    1. a)

      Explain evaluation measures (SSE, MSE) for regression model. For a given data having 100 examples, if squared errors SEI, SE2, and SE3 are 13.33, 3.33 and 4.00 respectively, calculate Mean Squared Error (MSE). State the formula for MSE.

      [6]
    2. b)

      Explain K nearest neighbor classification algorithm with suitable example.

      [6]
    3. c)

      What are kernel functions in SVM? Describe the Radial Basis Kernel, Gaussian, Polynomial, and Sigmoid kernel

      Also asked:Nov 2024

      [6]

  2. Q2

    1. a)

      Explain Baysian Linear Regression.

      [6]
    2. b)

      Differentiate between balanced and imbalanced classification.

      Also asked:Nov 2024

      [6]
    3. c)

      Explain the concept of a soft margin SVM. How does it differ from a hard margin SVM?

      [6]

  3. Q3

    1. a)

      Explain the DBSCAN algorithm. How does it work? What are its advantages and disadvantages.

      Also asked:Nov 2024

      [9]
    2. b)

      Discuss the applications of clustering techniques in market segmentaion, social network analysis, image segmentation, and anomaly detection. Provide examples for each application.

      [8]

  4. Q4

    1. a)

      Describe the Gaussian Mixture Model (GMM) for distribution-based clustering. How is it different from other clustering algorithms?

      [9]
    2. b)

      Explain the K-Means clustering algorithm. What are its advantages and disadvantages. 2

      Also asked:Nov 2023

      [8]

  5. Q5

    1. a)

      Explain the concept of ensemble learning. Why is ensemble learning considered beneficial in machine learning?

      [6]
    2. b)

      Discuss the concept of stacking in ensemble learning. What are the different methods used for variance reduction in stacking?

      [6]
    3. c)

      Describe the Random Forest ensemble method in detail.

      Also asked:Nov 2023

      [6]

  6. Q6

    1. a)

      Differentiate between homogeneous and heterogeneous ensemble methods. provide examples of each type.

      Also asked:Nov 2023

      [6]
    2. b)

      Describe the concept of a voting ensemble. What are the different types of voting techniques?

      [6]
    3. c)

      Explain the Adaptive Boosting (AdaBoost) algorithm in detail.

      [6]

  7. Q7

    1. a)

      Define reinforcement learning. Why is reinforcement learning important in the field of machine learning?

      [9]
    2. b)

      Explain Markov’s Decision Process (MDP). What is the Markov property in the context of MDP?

      [8]

  8. Q8

    1. a)

      Introduce Q-learning. What are the important terms used in Q-learning? How does Q-learning work?

      [9]
    2. b)

      Compare and contrast supervised, unsupervised, and reinforcement learning. Provide examples of each type. … … …

      [8]