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January 2026 term · Machine Learning Techniques · BSCS2007

Machine Learning Techniques End Term: 10 May 2026, Set 1-2 (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 1-2: 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
Numerical
9
MCQ
2
MSQ
5
Take as mock testPractise with answers

Updated

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

Question 1

+3 marksNumerical answer

Consider a dataset for a binary classification problem with five features, each of which is binary. If we go for a generative model that satisfies the class-conditional independence assumption, then how many free parameters do we need to learn?

Show answer

Correct answer: 11

Question 1 on its own page

Question 2

+3 marksNumerical answer
Show answer

Correct answer: 1230

Question 2 on its own page

Question 3

+3 marksNumerical answer
Show answer

Correct answer: 0.72 (accepted within ±0.01)

Question 3 on its own page

Question 4

+3 marksOne correct option
  1. A

    Hard-margin, Linear SVM

  2. B

    Hard-margin, Kernel SVM, with a suitable kernel

  3. C

    Soft-margin, Linear SVM

  4. D

    Soft-margin, Kernel SVM, with a suitable kernel

Show answer

Correct answer

  • D

    Soft-margin, Kernel SVM, with a suitable kernel

Question 4 on its own page

Question 5

+2 marksNumerical answer
Show answer

Correct answer: 0.3

Question 5 on its own page

Question 6

+2 marksNumerical answer
Show answer

Correct answer: 0.5

Question 6 on its own page

Question 7

+3 marksOne or more correct options

Select all that apply.

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

Correct answers

  • A
  • D
Question 7 on its own page

Question 8

+3 marksOne or more correct options

Two loss functions are plotted in this graph. What are the two?

Select all that apply.

  1. A

    Logistic loss

  2. B

    Hinge loss

  3. C

    Squared loss

  4. D

    0-1 loss

Show answer

Correct answers

  • A

    Logistic loss

  • B

    Hinge loss

Question 8 on its own page

Question 9

+3 marksOne or more correct options

Select all that apply.

  1. A
  2. B

    (A figure from the original paper is missing from the source site.)

  3. C
  4. D
Show answer

Correct answers

  • A
  • C
Question 9 on its own page

Question 10

+4 marksNumerical answer
Show answer

Correct answer: 10

Question 10 on its own page

Question 11

+4 marksNumerical answer
Show answer

Correct answer: 2

Question 11 on its own page

Question 12

+2 marksNumerical answer
Show answer

Correct answer: 0

Question 12 on its own page

Question 13

+2 marksNumerical answer
Show answer

Correct answer: 10

Question 13 on its own page

Question 14

+2 marksOne or more correct options

Which of the following is/are true?

Select all that apply.

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

Correct answers

  • B
  • D
Question 14 on its own page

Question 15

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

Correct answer

  • A
Question 15 on its own page

Question 16

+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 16 on its own page