Figure from the original question paper

(A) Option figure from the original question paper

(B) Option figure from the original question paper

(C) Option figure from the original question paper

(D) Option figure from the original question paper

You are evaluating the performance of a linear regression model using different hyperparameter settings (learning rate and number of iterations). Below are the results: | Setting | Learning Rate | Iterations | Training Error | Validation Error | |---|---|---|---|---| | 1 | 0.05 | 1000 | 150 | 160 | | 2 | 0.10 | 800 | 145 | 150 | | 3 | 0.15 | 500 | 140 | 190 | | 4 | 0.25 | 350 | 170 | 180 | Which hyperparameter setting would you choose based on the training and validation errors? Why?

(A) Setting 3, because it has the lowest training error, indicating the best fit to the training data.

(B) Setting 2, because it has the lowest validation error and a relatively low training error, indicating a good generalization.

(C) Setting 1, because it has the highest number of iterations and therefore likely to be more stable.

(D) Setting 4, because it uses the highest learning rate and converges the fastest.

Machine Learning Techniques Quiz 2: 3 August 2025 (May 2025 term)

Question 1

+4 marksOne correct option

Question 2

+3 marksOne correct option

In the Bayesian modeling for linear regression, we assume the following:

$y \mid x \sim \text{Normal}(w^Tx, \sigma^2 I)$
$w \sim \text{Normal}(0, \gamma^2 I)$

Under this setup, the maximum aposteriori (MAP) estimator of the weights $\hat{w}_{\text{MAP}}$ is given by:

$\hat{w}_{\text{MAP}} = \arg\min_{w} \left\{\|y - X^Tw\|^2 + \lambda\|w\|^2\right\}$

Using the given information, find the expression for the regularization parameter $\lambda$ .

Question 3

+3 marksOne correct option

You are evaluating the performance of a linear regression model using different hyperparameter settings (learning rate and number of iterations). Below are the results:

Setting	Learning Rate	Iterations	Training Error	Validation Error
1	0.05	1000	150	160
2	0.10	800	145	150
3	0.15	500	140	190
4	0.25	350	170	180

Which hyperparameter setting would you choose based on the training and validation errors? Why?

A
Setting 3, because it has the lowest training error, indicating the best fit to the training data.
B
Setting 2, because it has the lowest validation error and a relatively low training error, indicating a good generalization.
C
Setting 1, because it has the highest number of iterations and therefore likely to be more stable.
D
Setting 4, because it uses the highest learning rate and converges the fastest.

12 more questions in this paper

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More on the MLT Quiz 2 3 Aug 2025 paper

The IIT Madras BS Machine Learning Techniques (MLT) Quiz 2 paper sat on 3 Aug 2025, in the May 2025 term: 15 questions for 40 marks in 120 minutes. The first 3 questions are below. Sign in with Google — it is free — to see the whole paper with its answers and explanations, in learning mode or as a timed mock test.

Feature	MLT Quiz 2 3 Aug 2025 at a glance
Term	May 2025 term
Subject	Machine Learning Techniques
Course code	BSCS2007
Questions	15
Marks	40
Duration	120 min
MCQ	7
MSQ	2
Numerical	6
Official paper	IIT M DEGREE AN EXAM QDB2 03 Aug 2025
Negative marking	No negative marking.
Updated	25 Sept 2026