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January 2026 term · Machine Learning Techniques · BSCS2007

Machine Learning Techniques End Term: 10 May 2026, Set 1 (January 2026 term)

The IIT Madras BS Machine Learning Techniques (MLT) End Term paper sat on 10 May 2026, in the January 2026 term, set 1: 16 questions for 50 marks in 180 minutes. Every question is below with its answer. Take it as a timed mock test to be marked, or read it through first.

Questions
16
Marks
50
Duration
180 min
MCQ
6
MSQ
4
Numerical
6
Take as mock testPractise with answers

Updated

Official paper: Machine Learning Techniques 06 May 26 · No negative marking.

Question 1

+4 marksOne correct option
  1. A
  2. B
  3. C
  4. D
Show answer

Correct answer

  • A
Question 1 on its own page

Question 2

+4 marksOne or more correct options

Select all that apply.

  1. A
  2. B
  3. C
  4. D
Show answer

Correct answers

  • A
  • C
Question 2 on its own page

Question 3

+4 marksOne or more correct options

Select all that apply.

  1. A

    Linear regression without regularization will achieve training MSE less than or equal to that of ridge regression.

  2. B

    The solution for linear regression without regularization may not be unique.

  3. C

    Ridge regression (L2 regularization) will always set at least one coefficient exactly to zero.

  4. D

    Lasso regression (L1 regularization) can force some feature weights to become exactly zero.

Show answer

Correct answers

  • A

    Linear regression without regularization will achieve training MSE less than or equal to that of ridge regression.

  • B

    The solution for linear regression without regularization may not be unique.

  • D

    Lasso regression (L1 regularization) can force some feature weights to become exactly zero.

Question 3 on its own page

Question 4

+3 marksOne or more correct options

Select all that apply.

  1. A
  2. B

    The final weight vector obtained by the Perceptron algorithm depends on the initialization and the order in which the data points are presented.

  3. C

    The Perceptron algorithm is guaranteed to converge to some separating hyperplane in a finite number of updates.

  4. D

    The Perceptron algorithm always converges to the maximum margin separator among all possible separating hyperplanes.

Show answer

Correct answers

  • B

    The final weight vector obtained by the Perceptron algorithm depends on the initialization and the order in which the data points are presented.

  • C

    The Perceptron algorithm is guaranteed to converge to some separating hyperplane in a finite number of updates.

Question 4 on its own page

Question 5

+4 marksNumerical answer
Show answer

Correct answer: 1

Question 5 on its own page

Question 6

+4 marksNumerical answer
Show answer

Correct answer: 1

Question 6 on its own page

Question 7

+4 marksNumerical answer
Show answer

Correct answer: 0.465 (accepted within ±0.025)

Question 7 on its own page

Question 8

+2 marksNumerical answer

Based on the above data, answer the given subquestions.

Show answer

Correct answer: 5

Question 8 on its own page

Question 9

+2 marksNumerical answer

Based on the above data, answer the given subquestions.

Show answer

Correct answer: 6

Question 9 on its own page

Question 10

+2 marksOne correct option

Based on the above data, answer the given subquestions.

  1. A
  2. B
  3. C

    Tie

  4. D

    Cannot be determined

Show answer

Correct answer

  • B
Question 10 on its own page

Question 11

+2 marksOne or more correct options

Based on the above data, answer the given subquestions.

Select all that apply.

  1. A

    The nearest neighbors are dominated by positive class points.

  2. B

    The nearest neighbors are dominated by negative class points.

  3. C

    Positive class points are closer in all directions.

  4. D

    The classification is determined by majority voting among the nearest neighbors.

Show answer

Correct answers

  • B

    The nearest neighbors are dominated by negative class points.

  • D

    The classification is determined by majority voting among the nearest neighbors.

Question 11 on its own page

Question 12

+3 marksOne correct option

Consider the two decision trees shown in the figure.

Which of the following statements is correct?

  1. A

    Tree 1 has higher information gain than Tree 2

  2. B

    Tree 2 has higher information gain than Tree 1

  3. C

    Both trees have equal information gain

  4. D

    Cannot be determined without further splits

Show answer

Correct answer

  • A

    Tree 1 has higher information gain than Tree 2

Question 12 on its own page

Question 13

+3 marksOne correct option

Which of the following statements is correct?

  1. A

    Bagging reduces variance, Boosting reduces bias

  2. B

    Bagging reduces bias, Boosting reduces variance

  3. C

    Both reduce only variance

  4. D

    Both reduce only bias

Show answer

Correct answer

  • A

    Bagging reduces variance, Boosting reduces bias

Question 13 on its own page

Question 14

+3 marksOne correct option
  1. A
  2. B
  3. C
  4. D
Show answer

Correct answer

  • B
Question 14 on its own page

Question 15

+3 marksOne correct option
  1. A

    PCA has reduced the dimensionality of the dataset because the combinations of features are de-correlated

  2. B

    PCA has not reduced the dimensionality of the dataset because all principal components are retained.

  3. C

    PCA always reduces the dimensionality of the dataset regardless of the number of components retained.

  4. D

    PCA can never be used for dimensionality reduction.

Show answer

Correct answer

  • B

    PCA has not reduced the dimensionality of the dataset because all principal components are retained.

Question 15 on its own page

Question 16

+3 marksNumerical answer
Show answer

Correct answer: 260

Question 16 on its own page