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EndsemNov 20252019 patternTE IT

Artificial Intelligence & Machine Learning (AIML) 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 AIML questions
Subject code
314443
Pattern
2019 pattern
Exam
Endsem
Session
Nov 2025
Max. marks
70
Time
2½ Hours
Semester
V
Paper code
[6583]-129

Printed for: T.E. (IT)

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 side indicate full marks.
  4. Assume suitable data, if necessary.

Questions

  1. Q1

    1. a)

      What do you understand by overfitting data? Gives any two methods to avoid overfitting.

      [6]
    2. b)

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

      [5]
    3. c)

      Compare the linear regression model and the logistic regression model.

      [6]

  2. Q2

    1. a)

      What are the metrics used to validate the result of the regression and explain each?

      [6]
    2. b)

      Distinguish between overfitting and underfitting. How it can affect model generalization.

      [5]
    3. c)

      Discuss linear regression with examples. Explain the role of the hypothesis function in ML.

      [6]

  3. Q3

    1. a)

      What is Gini impurity? Briefly explain the properties of Gini Impurity.

      [8]
    2. b)

      [CO4, L 1 ] How does the decision tree handle missing attribute Values?

      [4]
    3. c)

      [CO4, L 2 ] What do you understand by Information gain? Explain the mathematical formula associated with it.

      [6]

  4. Q4

    1. a)

      Write short notes on i) Conditional probability ii) Tree Pruning iii) Bayesian Network

      [12]
    2. b)

      What do the root node, leaf node, and parent node represent in a decision tree model? Represent them pictorially.

      [6]

  5. Q5

    1. a)

      What is the difference between the Manhattan distance and Euclidean distance?

      [4]
    2. b)

      Explain in detail Apriori Algorithm.

      [8]
    3. c)

      Define the following terms concerning K - Nearest Neighbor Learning i) Regression ii) Residual iii) Kernel Function

      [6]

  6. Q6

    1. a)

      What is the difference between k-Means and K-Medians and when would you use one over another?

      [6]
    2. b)

      What is hierarchical clustering? Explain the issue of connectivity constraints.

      [8]
    3. c)

      What are support and confidence measures in data mining?

      [4]

  7. Q7

    1. a)

      With suitable equations, explain any two types of activation functions used in a neural network.

      [8]
    2. b)

      Explain why the initialization process of weight and bias is important for neural networks.

      [6]
    3. c)

      Write short notes on Mean square Error.

      Also asked:May 2025

      [3]

  8. Q8

    1. a)

      Explain with a neat diagram how neural networks are modeled.

      [6]
    2. b)

      Write short notes on i) Sigmoidal Neuron ii) McCulloch Pitts Neuron

      [6]
    3. c)

      Explain the working of a Perceptron with a neat diagram. ♣♣♣

      [5]