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EndsemMay 20232019 patternBE Comp

Machine Learning & Data Science (MLDS) SPPU Endsem Question Paper May 2023

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

Open the PDFMost repeated MLDS questions
Subject code
410501
Pattern
2019 pattern
Exam
Endsem
Session
May 2023
Max. marks
70
Time
2½ Hours
Semester
VII
Paper code
[6004]-701

Printed for: B.E. (Computer Engineering) (Honours in Data Science)

Instructions to the candidates

  1. Answer Q. 1 or Q. 2, Q. 3 or Q. 4 , Q. 5 or Q. 6, Q.7 or Q.8
  2. Neat diagrams must be drawn wherever necessary
  3. Figures to the right indicate full marks
  4. Assume suitable data, if necessary

Questions

  1. Q1

    1. a)

      Explain unsupervised learning.

      [6]
    2. b)

      What do you mean by divisive clustering techniques? Explain with an example.

      [6]
    3. c)

      What is the role of dendrograms in choosing number clusters in hierarchical clustering?

      Also asked:Nov 2022

      [6]

  2. Q2

    1. a)

      What are the types of hierarchical clustering methods? Explain.

      [6]
    2. b)

      For what type of data Density-Based Spatial Clustering is suitable? Which parameters are required by DBSCAN algorithm?

      [6]
    3. c)

      Explain K-Medians clustering algorithm.

      [6]

  3. Q3

    1. a)

      Explain a biological neuron along with its parts.

      [4]
    2. b)

      What is the difference between Forward propagation and Backward Propagation in Neural Networks?

      [6]
    3. c)

      What is the role of the Activation functions in Neural Networks? List down the names of some popular Activation Functions used in Neural Networks. 2

      Also asked:Nov 2022

      [7]

  4. Q4

    1. a)

      Enlist limitations of MLP.

      [4]
    2. b)

      Explain the process of training a perceptron.

      [6]
    3. c)

      Explain back propagation algorithm.

      [7]

  5. Q5

    1. a)

      Does the size of the feature map always reduce upon applying the filters? Explain why or why not.

      [6]
    2. b)

      Illustrate Gradient descent optimization using an example.

      Also asked:Nov 2022

      [6]
    3. c)

      Explain Recurrent Neural Network

      Also asked:Nov 2022

      [6]

  6. Q6

    1. a)

      Explain Recursive Neural Network

      [6]
    2. b)

      Explain the different layers in CNN. Explain the significance of the RELU Activation function in Convolution Neural Network.

      Also asked:Nov 2022

      [6]
    3. c)

      Illustrate Long-short Term Memory along with its structure.

      Also asked:Nov 2022

      [6]

  7. Q7

    1. a)

      What are various text similarity measures? Explain any two of them.

      Also asked:Nov 2022

      [6]
    2. b)

      Write short note on i) Stemming ii) Lemmatization

      [6]
    3. c)

      What are the practical uses of feature extraction?

      Also asked:Nov 2022

      [5]

  8. Q8

    1. a)

      What do you mean by topic modeling? Explain Latent Dirichlet Allocation.

      [6]
    2. b)

      Explain feature selection and extraction.

      [6]
    3. c)

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

      [5]