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EndsemNov 20232019 patternBE AI&DS

Machine Learning (ML) SPPU Endsem Question Paper Nov 2023

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 2023
Max. marks
70
Time
2½ Hours
Semester
VII
Paper code
[6181]-404

Printed for: B.E. (AI & DS)

Instructions to the candidates

  1. Answer Q1 or Q2, Q3 or Q4, Q5 or Q6, Q7 or Q8.
  2. Neat diagrams must be drawn wherever necessary.
  3. Figures to right indicate full marks.

Questions

  1. Q1

    1. a)

      This part has equations that don’t show as text. Open the PDF to read it.

      [12]
    2. b)

      Explain support Vector Machine classification algorithm with suitable example.

      [6]

  2. Q2

    1. a)

      Explain any 4 evaluation measures of Binary classification with example?

      Also asked:Nov 2024

      [6]
    2. b)

      Explain construction of multi-classifier. i) One Vs. All approach ii) One Vs One approach

      [6]
    3. c)

      This part has equations that don’t show as text. Open the PDF to read it.

      [6]

  3. Q3

    1. a)

      Explain K - Means clustering algorithm and states the advantages and disadvantages of k-means clustering algorithm.

      Also asked:Nov 2025

      [9]
    2. b)

      Explain Gaussian mixture model with example.

      [8]

  4. Q4

    1. a)

      Elaborate need of clustering and explain how the elbow method is used to decide the value of cluster k.

      [9]
    2. b)

      Explain Divisive Hierarchical clustering (DHC) algorithm with example.

      [8]

  5. Q5

    1. a)

      Differentiate the Bagging and Boosting approach of ensemble learning.

      [6]
    2. b)

      Explain different types of voting mechanisms in ensemble learning.

      [6]
    3. c)

      Explain AdaBoost algorithm in detail.

      [6]

  6. Q6

    1. a)

      Compare Homogeneous and Heterogeneous ensemble methods.

      Also asked:Nov 2025

      [6]
    2. b)

      What is the ensemble learning? Explain any two ensemble learning techniques.

      [6]
    3. c)

      Explain random forest ensembles with an example.

      Also asked:Nov 2025

      [6]

  7. Q7

    1. a)

      Explain following terms: i) Markov Property ii) Bellman Equation iii) Markov Reward Process iv) Markov Chain

      [8]
    2. b)

      Explain Q-Learning algorithm with an example.

      [9]

  8. Q8

    1. a)

      What is Reinforcement Learning? Explain the real time applications of reinforcement learning.

      [8]
    2. b)

      Explain following terms : i) Supervised Learning. ii) Unsupervised Learning. iii) Reinforcement Learning. 

      [9]