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MLT · Most repeated questions

87 questions · 230 marks · 7 papers · 40 question types

Answer key

Indian Institute of Technology Madras

BS Degree Programme · Examination & Study Paper

Machine Learning Techniques

Most repeated questions

Official Paper

Examination Instructions & Guidelines

Solve on notebook/paper first before checking solutions.
MCQ: Exactly 1 option is correct. Negative marking applies.
MSQ: One or more options correct. Select all to score.
NAT: Numerical response. No negative marking.
Scientific calculator allowed for all calculations.
Official answer key is sealed at the end of this sheet.

Type 1 · asked 4 times · Neural networks

Neural network forward pass with ReLU/sigmoid for a given input

Q1NAT4 Marks·Write the number
End Term · 13 Apr 2025 (AN)Key ↓
Your answer:
Read the passage below, then answer Q2
Based on the above data, answer the given subquestions.
Q2NAT3 Marks·Write the number
End Term · 13 Apr 2025 (FN)Key ↓
Your answer:
Q3MCQ4 Marks·Choose one option
End Term · 31 Aug 2025 (AN)Key ↓
1. A
2. B
3. C
4. D
Q4NAT4 Marks·Write the number
End Term · 31 Aug 2025 (FN)Key ↓
Your answer:

Type 2 · asked 3 times · MLE, MAP & Bayesian estimation

Find the MAP estimate of a parameter with a uniform prior

Q5NAT3 Marks·Write the number
End Term · 22 Dec 2024 (AN)Key ↓
Your answer:
Q6NAT3 Marks·Write the number
End Term · 22 Dec 2024 (FN)Key ↓
Your answer:
Q7NAT4 Marks·Write the number
End Term · 31 Aug 2025 (AN)Key ↓
Your answer:

Type 3 · asked 3 times · K-means clustering

Statements on k-means / Lloyd's algorithm: init, outliers, convergence

Q8MSQ3 Marks·Choose all correct options
End Term · 22 Dec 2024 (AN)Key ↓
Select the correct statements from the following for k-means algorithm:
1. AIn k-means algorithm, all cluster initializations lead to the same result.
2. Bk-means algorithm is sensitive to outliers.
3. COne initialization may converge while another may not.
4. DThe initialization of cluster centres may affect the number of iterations k- means takes to converge.
Q9MSQ2 Marks·Choose all correct options
End Term · 31 Aug 2025 (FN)Key ↓
With respect to the Lloyd’s algorithm, choose the correct statements:
1. AThe partition configurations cannot repeat themselves.
2. BAfter doing the reassignments (consider at least one point reassigned to the new cluster), we might get the same means for all clusters.
3. CObjective function after making the re-assignments strictly reduces.
4. DObjective function after making the re-assignments strictly increases.
Q10MSQ2 Marks·Choose all correct options
Quiz 1 · 26 Oct 2025 (AN)Key ↓
Which among the following is true with respect to the Lloyds’ algorithm?
1. AIt always converges.
2. BIt may fail to converge for an initialization while it may converge for other initialization.
3. CIt is sensitive to outliers.
4. DThe solution produced is always the optimal.

Type 4 · asked 3 times · K-means clustering

Run Lloyd's (k-means) algorithm from given initial clusters or centres

Q11MCQ3 Marks·Choose one option
End Term · 13 Apr 2025 (AN)Key ↓
1. A
2. B
3. C
Q12NAT3 Marks·Write the number
End Term · 13 Apr 2025 (FN)Key ↓
Your answer:
Read the passage below, then answer Q13
Based on the above data, answer the given subquestions.
Q13MCQ3 Marks·Choose one option
Quiz 1 · 26 Oct 2025 (AN)Key ↓
Perform K-means clustering with K = 2 and initial cluster centers at (−2, 3) and (2,−1). What are the final means of clusters after convergence?
1. A
2. B
3. C
4. D

Type 5 · asked 3 times · Perceptron algorithm

Run the perceptron on a small dataset cycling left to right

Q14MSQ4 Marks·Choose all correct options
End Term · 13 Apr 2025 (AN)Key ↓
1. A
2. B
3. C
4. D
5. E
6. F
Read the passage below, then answer Q15
Based on the above data, answer the given subquestions.
Q15NAT3 Marks·Write the number
End Term · 13 Apr 2025 (FN)Key ↓
Your answer:
Q16MCQ3 Marks·Choose one option
End Term · 31 Aug 2025 (AN)Key ↓
1. AYes
2. BNo

Type 6 · asked 3 times · Bagging, boosting & random forests

Run one round of AdaBoost: pick the stump and update weights

Read the passage below, then answer Q17
Based on the above data, answer the given subquestions.
Q17MCQ3 Marks·Choose one option
End Term · 13 Apr 2025 (AN)Key ↓
1. A
2. B
3. C
4. D
Q18MCQ4 Marks·Choose one option
End Term · 13 Apr 2025 (FN)Key ↓
1. A
2. B
3. C
4. D
Q19NAT4 Marks·Write the number
End Term · 31 Aug 2025 (FN)Key ↓
Your answer:

Type 7 · asked 2 times · PCA, kernel PCA & kernels

Variance along the first PC for the dataset {-2u, -u, u, 2u}

Q20NAT2 Marks·Write the number
End Term · 22 Dec 2024 (AN)Key ↓
NOTE: Enter the answer correct to one decimal place
Your answer:
Q21NAT2 Marks·Write the number
End Term · 22 Dec 2024 (FN)Key ↓
NOTE: Enter the answer correct to one decimal place
Your answer:

Type 8 · asked 2 times · PCA, kernel PCA & kernels

PCA reconstruction error when keeping the top two components

Q22NAT3 Marks·Write the number
End Term · 22 Dec 2024 (AN)Key ↓
Your answer:
Q23NAT3 Marks·Write the number
End Term · 22 Dec 2024 (FN)Key ↓
Your answer:

Type 9 · asked 2 times · MLE, MAP & Bayesian estimation

Posterior mean of a probability with a Beta prior

Q24NAT4 Marks·Write the number
End Term · 13 Apr 2025 (AN)Key ↓
Your answer:
Q25MSQ4 Marks·Choose all correct options
Quiz 1 · 26 Oct 2025 (AN)Key ↓
1. APosterior mean for Beta(1, 1) is 0.039.
2. BPosterior mean for Beta(1, 1) is 0.03.
3. CPosterior mean for Beta(0.5, 0.5) is 0.025.
4. DPosterior mean for Beta(0.5, 0.5) is 0.034.

Type 10 · asked 2 times · Logistic regression

Logistic regression spam detector: threshold and error trade-off

Q26NAT3 Marks·Write the number
End Term · 22 Dec 2024 (AN)Key ↓
Consider a logistic regression model trained to detect spam emails. Emails containing harmful phishing links are labeled as spam (1), while regular emails are labeled as not spam (0). A good spam detector should correctly identify almost all emails with phishing links. Even a single phishing email incorrectly classified as not spam could expose users to significant risks. However, the detector may classify some regular emails as spam. This trade-off is acceptable to ensure safety. To ensure user safety, if the threshold is set to a low value to prioritize the detection of phishing emails, type 1. If the threshold is set to a high value to minimize false positives (misclassifying regular emails as spam), type 0.
Your answer:
Q27NAT3 Marks·Write the number
End Term · 22 Dec 2024 (FN)Key ↓
Consider a logistic regression model trained to detect spam emails. Emails containing harmful phishing links are labeled as spam (1), while regular emails are labeled as not spam (0). A good spam detector should correctly identify almost all emails with phishing links. Even a single phishing email incorrectly classified as not spam could expose users to significant risks. However, the detector may classify some regular emails as spam. This trade-off is acceptable to ensure safety. To ensure user safety, if the threshold is set to a low value to prioritize the detection of phishing emails, type 1. If the threshold is set to a high value to minimize false positives (misclassifying regular emails as spam), type 0.
Your answer:

Type 11 · asked 2 times · Bias-variance, overfitting & validation

Identify training vs validation loss curves

Q28MSQ3 Marks·Choose all correct options
End Term · 22 Dec 2024 (AN)Key ↓
1. ACurve (A) represents the training loss.
2. BCurve (B) represents the training loss.
3. CCurve (A) represents the validation loss.
4. DCurve (B) represents the validation loss.
Q29MSQ3 Marks·Choose all correct options
End Term · 22 Dec 2024 (FN)Key ↓
Consider a supervised ML problem where a model is trained using a training dataset, and its performance is evaluated on both the training and validation datasets. During training, the model’s loss is plotted over time for both the datasets. Assume that the loss L(t) is decreasing as the model learns. The following graph shows two curves, Curve (A) and Curve (B), representing the loss for the training and validation datasets, respectively:
1. ACurve (A) represents the training loss.
2. BCurve (B) represents the training loss.
3. CCurve (A) represents the validation loss.
4. DCurve (B) represents the validation loss.

Type 12 · asked 2 times · Linear regression

One gradient descent step for linear regression

Q30MCQ4 Marks·Choose one option
End Term · 22 Dec 2024 (AN)Key ↓
1. A
2. B
3. C
4. D
Q31MCQ4 Marks·Choose one option
End Term · 22 Dec 2024 (FN)Key ↓
1. A
2. B
3. C
4. D

Type 13 · asked 2 times · Perceptron algorithm

Perceptron: weights after the next two update points

Q32MCQ4 Marks·Choose one option
End Term · 22 Dec 2024 (AN)Key ↓
1. A
2. B
3. C
4. D
Q33MCQ4 Marks·Choose one option
End Term · 22 Dec 2024 (FN)Key ↓
1. A
2. B
3. C
4. D

Type 14 · asked 2 times · Naive Bayes & generative models

Naive Bayes passage: find a + b from estimated parameters

Read the passage below, then answer Q34
Q34NAT2 Marks·Write the number
End Term · 22 Dec 2024 (AN)Key ↓
Your answer:
Read the passage below, then answer Q35
Q35NAT2 Marks·Write the number
End Term · 22 Dec 2024 (FN)Key ↓
Your answer:

Type 15 · asked 2 times · Naive Bayes & generative models

Naive Bayes passage: find b + d + f from estimated parameters

Read the passage below, then answer Q36
Q36NAT2 Marks·Write the number
End Term · 22 Dec 2024 (AN)Key ↓
Your answer:
Read the passage below, then answer Q37
Q37NAT2 Marks·Write the number
End Term · 22 Dec 2024 (FN)Key ↓
Your answer:

Type 16 · asked 2 times · Naive Bayes & generative models

Naive Bayes passage: predict the label for test point [1,0,1]

Read the passage below, then answer Q38
Q38NAT2 Marks·Write the number
End Term · 22 Dec 2024 (AN)Key ↓
Your answer:
Read the passage below, then answer Q39
Q39NAT2 Marks·Write the number
End Term · 22 Dec 2024 (FN)Key ↓
Your answer:

Type 17 · asked 2 times · Bias-variance, overfitting & validation

LOOCV error for the model f = beta x on three points

Q40NAT5 Marks·Write the number
End Term · 22 Dec 2024 (AN)Key ↓
Your answer:
Q41NAT5 Marks·Write the number
End Term · 22 Dec 2024 (FN)Key ↓
Your answer:

Type 18 · asked 2 times · Support vector machines

Evaluate max over lambda of the Lagrangian at a given point

Q42MCQ3 Marks·Choose one option
End Term · 22 Dec 2024 (AN)Key ↓
1. A
2. B
3. C
4. D
5. E
Q43MCQ3 Marks·Choose one option
End Term · 22 Dec 2024 (FN)Key ↓
1. A
2. B
3. C
4. D
5. E

Type 19 · asked 2 times · Bias-variance, overfitting & validation

Bias, variance, weak learners and bagging statements

Q44MCQ3 Marks·Choose one option
End Term · 22 Dec 2024 (AN)Key ↓
Which of the following options are not correct? Select all that apply.
1. AUnderfitted models generally have less bias and high variance.
2. BA decision tree with the maximum possible length may lead to overfitting.
3. CWeak learners are those whose performance is slightly better than the random classifier.
4. DAll the estimators in bagging can be trained parallelly.
Q45MSQ3 Marks·Choose all correct options
End Term · 22 Dec 2024 (FN)Key ↓
Which of the following options are correct? Select all that apply.
1. AUnderfitted models generally have less bias and high variance.
2. BA decision tree with the maximum possible length may lead to overfitting.
3. CWeak learners are those whose performance is slightly better than the random classifier.
4. DAll the estimators in bagging can be trained parallelly.

Type 20 · asked 2 times · Support vector machines

Effect of removing non-support vectors on an SVM

Q46MCQ2 Marks·Choose one option
End Term · 22 Dec 2024 (AN)Key ↓
If we remove all the non-support vectors from the dataset, what will be the impact on the model using SVM algorithm?
1. AModel will overfit.
2. BThe model will not be changed.
3. CModel will underfit.
4. DAccuracy of the model will increase.
Q47MCQ2 Marks·Choose one option
End Term · 22 Dec 2024 (FN)Key ↓
If we remove all the non-support vectors from the dataset, what will be the impact on the model using SVM algorithm?
1. AModel will overfit.
2. BThe model will not be changed.
3. CModel will underfit.
4. DAccuracy of the model will increase.

Type 21 · asked 2 times · Bagging, boosting & random forests

When does AdaBoost decrease a training point's weight

Q48MCQ2 Marks·Choose one option
End Term · 22 Dec 2024 (AN)Key ↓
In each round of AdaBoost, the weight for a particular training observation is decreased from round t to round t + 1 if the observation was:
1. Aclassified incorrectly by the weak learner trained in the round.
2. Bclassified correctly by the weak learner trained in the round.
3. Cclassified incorrectly by a majority of the weak learners trained up to the round.
4. Dclassified correctly by a majority of the weak learners trained up to the round.
Q49MCQ2 Marks·Choose one option
End Term · 22 Dec 2024 (FN)Key ↓
In each round of AdaBoost, the weight for a particular training observation is decreased from round t to round t + 1 if the observation was:
1. Aclassified incorrectly by the weak learner trained in the round.
2. Bclassified correctly by the weak learner trained in the round.
3. Cclassified incorrectly by a majority of the weak learners trained up to the round.
4. Dclassified correctly by a majority of the weak learners trained up to the round.

Type 22 · asked 2 times · Bias-variance, overfitting & validation

Does more model complexity always raise test error

Q50MCQ1 Mark·Choose one option
End Term · 22 Dec 2024 (AN)Key ↓
Is the following statement true or false? For a fixed size of the training and the test set, increasing the complexity of the model always leads to an increment in the test error.
1. ATrue
2. BFalse
Q51MCQ1 Mark·Choose one option
End Term · 22 Dec 2024 (FN)Key ↓
Is the following statement true or false? For a fixed size of the training and the test set, increasing the complexity of the model always leads to an increment in the test error.
1. ATRUE
2. BFALSE

Type 23 · asked 2 times · Support vector machines

Soft-margin SVM slack value xi_4

Read the passage below, then answer Q52
Q52NAT0.5 Marks·Write the number
End Term · 22 Dec 2024 (AN)Key ↓
Your answer:
Read the passage below, then answer Q53
Q53NAT0.5 Marks·Write the number
End Term · 22 Dec 2024 (FN)Key ↓
Your answer:

Type 24 · asked 2 times · Support vector machines

Soft-margin SVM slack value xi_1

Read the passage below, then answer Q54
Q54NAT0.5 Marks·Write the number
End Term · 22 Dec 2024 (AN)Key ↓
Your answer:
Read the passage below, then answer Q55
Q55NAT0.5 Marks·Write the number
End Term · 22 Dec 2024 (FN)Key ↓
Your answer:

Type 25 · asked 2 times · Support vector machines

Soft-margin SVM slack value xi_2

Read the passage below, then answer Q56
Q56NAT0.5 Marks·Write the number
End Term · 22 Dec 2024 (AN)Key ↓
Your answer:
Read the passage below, then answer Q57
Q57NAT0.5 Marks·Write the number
End Term · 22 Dec 2024 (FN)Key ↓
Your answer:

Type 26 · asked 2 times · Support vector machines

Soft-margin SVM slack value xi_6

Read the passage below, then answer Q58
Q58NAT0.5 Marks·Write the number
End Term · 22 Dec 2024 (AN)Key ↓
Your answer:
Read the passage below, then answer Q59
Q59NAT0.5 Marks·Write the number
End Term · 22 Dec 2024 (FN)Key ↓
Your answer:

Type 27 · asked 2 times · Support vector machines

Soft-margin SVM: range of xi_5 and xi_7

Read the passage below, then answer Q60
Q60MSQ1 Mark·Choose all correct options
End Term · 22 Dec 2024 (AN)Key ↓
Which of the following is correct ?
1. A
2. B
3. C
4. D
Read the passage below, then answer Q61
Q61MSQ1 Mark·Choose all correct options
End Term · 22 Dec 2024 (FN)Key ↓
Which of the following is correct ?
1. A
2. B
3. C
4. D

Type 28 · asked 2 times · Ridge & lasso regularization

Effect of the regularization term / lambda in ridge and lasso

Q62MSQ2 Marks·Choose all correct options
End Term · 13 Apr 2025 (AN)Key ↓
What is the role of the regularization term in ridge regression?
1. AIt ensures that the dataset is normally distributed.
2. BIt penalizes large coefficients to reduce overfitting.
3. CIt forces more coefficients to be exactly zero, performing feature selection.
4. DIt increases the variance of the model.
Q63MSQ2 Marks·Choose all correct options
End Term · 13 Apr 2025 (FN)Key ↓
1. AIt penalizes large coefficients to reduce overfitting.
2. BIt shrinks the coefficients but does not set them to zero.
3. CIt forces more coefficients to be exactly zero, performing feature selection.
4. DIt has no effect on the regression model.

Type 29 · asked 2 times · Ridge & lasso regularization

Match weight vectors to regularization strength

Q64MCQ3 Marks·Choose one option
End Term · 13 Apr 2025 (AN)Key ↓
1. A
2. B
3. C
4. D
Q65MCQ3 Marks·Choose one option
End Term · 13 Apr 2025 (FN)Key ↓
1. A
2. B
3. C

Type 30 · asked 2 times · Ridge & lasso regularization

Compute the ML and ridge weight estimates for a given lambda

Read the passage below, then answer Q66
Based on the above data, answer the given subquestions.
Q66MCQ4 Marks·Choose one option
End Term · 31 Aug 2025 (AN)Key ↓
1. A
2. B
3. C
4. D
Read the passage below, then answer Q67
Based on the above data, answer the given subquestions.
Q67MCQ4 Marks·Choose one option
End Term · 31 Aug 2025 (FN)Key ↓
1. A
2. B
3. C
4. D

Type 31 · asked 2 times · Ridge & lasso regularization

How ridge coefficients change as lambda changes

Read the passage below, then answer Q68
Based on the above data, answer the given subquestions.
Q68MCQ2 Marks·Choose one option
End Term · 31 Aug 2025 (AN)Key ↓
1. AThe coefficients shrinks towards zero.
2. BThe coefficients increases in magnitude.
3. CThe coefficients remain unchanged.
Read the passage below, then answer Q69
Based on the above data, answer the given subquestions.
Q69MCQ2 Marks·Choose one option
End Term · 31 Aug 2025 (FN)Key ↓
1. AThe coefficients shrinks towards zero.
2. BThe coefficients increases in magnitude.
3. CThe coefficients remain unchanged.

Type 32 · asked 2 times · Support vector machines

Soft-margin SVM: statements about optimal primal and dual solutions

Q70MSQ3 Marks·Choose all correct options
End Term · 13 Apr 2025 (AN)Key ↓
1. A
2. B
3. C
4. D
5. E
Q71MSQ3 Marks·Choose all correct options
End Term · 31 Aug 2025 (FN)Key ↓
1. A
2. B
3. C
4. D

Type 33 · asked 2 times · Support vector machines

Soft-margin SVM formulation: which statements are correct

Q72MSQ3 Marks·Choose all correct options
End Term · 13 Apr 2025 (FN)Key ↓
1. A
2. B
3. C
4. D
5. E
Q73MSQ3 Marks·Choose all correct options
End Term · 31 Aug 2025 (AN)Key ↓
1. A
2. B
3. C
4. D

Type 34 · asked 2 times · Support vector machines

Use the SVM dual solution alpha* to get w, then predict or find margin

Q74NAT3 Marks·Write the number
End Term · 13 Apr 2025 (AN)Key ↓
Your answer:
Read the passage below, then answer Q75
Consider the datapoints (−2,−2) with label +1 and (2, 2) with label −1. A hard margin SVM is applied on this dataset. Answer the given subquestions:
Q75NAT2 Marks·Write the number
End Term · 31 Aug 2025 (FN)Key ↓
Your answer:

Type 35 · asked 2 times · Support vector machines

Identify which points are (not) support vectors

Read the passage below, then answer Q76
Based on the above data, answer the given subquestions.
Q76MSQ3 Marks·Choose all correct options
End Term · 13 Apr 2025 (FN)Key ↓
Which of the following training data points are certainly not support vectors?
1. A
2. B
3. C
4. D
Q77MCQ2 Marks·Choose one option
End Term · 31 Aug 2025 (AN)Key ↓
1. A
2. B
3. C
4. D

Type 36 · asked 2 times · Naive Bayes & generative models

Count the parameters a naive Bayes model must estimate

Q78NAT3 Marks·Write the number
End Term · 13 Apr 2025 (FN)Key ↓
Your answer:
Read the passage below, then answer Q79
Based on the above data, answer the given subquestions.
Q79NAT1 Mark·Write the number
End Term · 31 Aug 2025 (AN)Key ↓
Your answer:

Type 37 · asked 2 times · Naive Bayes & generative models

Naive Bayes class-conditional probability with Laplace smoothing

Read the passage below, then answer Q80
Based on the above data, answer the given subquestions.
Q80NAT3 Marks·Write the number
End Term · 31 Aug 2025 (AN)Key ↓
Your answer:
Q81NAT3 Marks·Write the number
End Term · 31 Aug 2025 (FN)Key ↓
Your answer:

Type 38 · asked 2 times · Logistic regression

Logistic regression: predicted probability or label for a point

Q82NAT3 Marks·Write the number
End Term · 13 Apr 2025 (AN)Key ↓
Your answer:
Read the passage below, then answer Q83
Based on the above data, answer the given subquestions.
Q83NAT3 Marks·Write the number
End Term · 31 Aug 2025 (FN)Key ↓
What is the probability that a 60-year-old patient with a cholesterol level of 200 mg/dL has the disease? Enter the answer correct to two decimal places.
Your answer:

Type 39 · asked 2 times · Logistic regression

Logistic regression: feature value giving a target probability

Q84NAT3 Marks·Write the number
End Term · 31 Aug 2025 (AN)Key ↓
Your answer:
Read the passage below, then answer Q85
Based on the above data, answer the given subquestions.
Q85NAT3 Marks·Write the number
End Term · 31 Aug 2025 (FN)Key ↓
How high should the cholesterol level be for the patient mentioned in the previous question to have a 50% chance of being diagnosed with the disease?
Your answer:

Type 40 · asked 2 times · PCA, kernel PCA & kernels

Find the feature map phi for a given polynomial kernel

Q86MCQ3 Marks·Choose one option
End Term · 31 Aug 2025 (AN)Key ↓
1. A
2. B
3. C
4. D
Q87MCQ3 Marks·Choose one option
End Term · 31 Aug 2025 (FN)Key ↓
1. A
2. B
3. C
4. D

Official Key

Master Answer Key & Explanations

Official answer keys from IIT Madras BS past papers. Click any question to review.

Reveal Key

Q14

Q20.64 to 0.68

Q3D

Q40.69 to 0.696

Q552

Q655

Q70.28 to 0.34

Q8B, D

Q9A, C

Q10A, C

Q11A

Q1224

Q13A

Q14B, E

Q151

Q16B

Q17B

Q18B

Q190.5

Q202.5

Q212.5

Q220

Q230

Q240.148 to 0.154

Q25A, D

Q261

Q271

Q28A, D

Q29A, D

Q30B

Q31D

Q32A

Q33A

Q341

Q351

Q362

Q372

Q381

Q391

Q400.3 to 0.4

Q412

Q42A

Q43B

Q44A

Q45B, C, D

Q46B

Q47B

Q48B

Q49B

Q50B

Q51B

Q521

Q531

Q540

Q550

Q560

Q570

Q582

Q592

Q60A, B

Q61A, B

Q62B

Q63A, C

Q64B

Q65C

Q66C

Q67D

Q68B

Q69A

Q70B, D, E

Q71B, D

Q72B, D, E

Q73A, D

Q741

Q755.64 to 5.68

Q76A, C

Q77B

Q7811

Q797

Q800.0625

Q810.1 to 0.14

Q821

Q830.71 to 0.75

Q841.1 to 1.14

Q85150

Q86C

Q87C

Questions and answer keys are reproduced from the official IIT Madras BS papers. Topics and repeated question types are BTechi’s own grouping. BTechi is not affiliated with IIT Madras.