Introduction to Machine Learning Week 5 NPTEL Assignment Answers 2025

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✅ Subject: Introduction to Machine Learning (nptel ml Answers)
📅 Week: 5
🎯 Session: NPTEL 2025 July-October
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NPTEL Introduction to Machine Learning Week 5 Assignment Answers 2025

1. Given a 3 layer neural network which takes in 10 inputs, has 5 hidden units and outputs 10 outputs, how many parameters are present in this network?

• 115
• 500
• 25
• 100

Answer : See Answers

2. Recall the XOR(tabulated below) example from class where we did a transformation of features to make it linearly separable. Which of the following transformations can also work?

• Rotating x₁ and x₂ by a fixed angle.
• Adding a third dimension z=x∗y
• Adding a third dimension z=x²+y²
• None of the above

Answer :

3. We use several techniques to ensure the weights of the neural network are small (such as random initialization around 0 or regularisation). What conclusions can we draw if weights of our ANN are high?

• (a) Model has overfitted.
• (b) It was initialized incorrectly.
• At least one of (a) or (b).
• None of the above.

Answer :

4. In a basic neural network, which of the following is generally considered a good initialization strategy for the weights?

• Initialize all weights to zero
• Initialize all weights to a constant non-zero value (e.g., 0.5)
• Initialize weights randomly with small values close to zero
• Initialize weights with large random values (e.g., between -10 and 10)

Answer :

5. Which of the following is the primary reason for rescaling input features before passing them to a neural network?

• To increase the complexity of the model
• To ensure all input features contribute equally to the initial learning process
• To reduce the number of parameters in the network
• To eliminate the need for activation functions

Answer :

6.

• P(θ|D) is the likelihood, P(D|θ) is the posterior, P(θ) is the prior, P(D) is the evidence
• P(θ|D) is the posterior, P(D|θ) is the likelihood, P(θ) is the prior, P(D) is the evidence
• P(θ|D) is the evidence, P(D|θ) is the likelihood, P(θ) is the posterior, P(D) is the prior
• P(θ|D) is the prior, P(D|θ) is the evidence, P(θ) is the likelihood, P(D) is the posterior

Answer : See Answers

7. Why do we often use log-likelihood maximization instead of directly maximizing the likelihood in statistical learning?

• Log-likelihood provides a different optimal solution than likelihood maximization
• Log-likelihood is always faster to compute than likelihood
• Log-likelihood turns products into sums, making computations easier and more numerically stable
• Log-likelihood allows us to avoid using probability altogether

Answer :

8. In machine learning, if you have an infinite amount of data, but your prior distribution is incorrect, will you still converge to the right solution?

• Yes, with infinite data, the influence of the prior becomes negligible, and you will converge to the true underlying solution.
• No, the incorrect prior will always affect the convergence, and you may not reach the true solution even with infinite data.
• It depends on the type of model used; some models may still converge to the right solution, while others might not.
• The convergence to the right solution is not influenced by the prior, as infinite data will always lead to the correct solution regardless of the prior.

Answer :

9. Statement: Threshold function cannot be used as activation function for hidden layers.
Reason: Threshold functions do not introduce non-linearity.

• Statement is true and reason is false.
• Statement is false and reason is true.
• Both are true and the reason explains the statement.
• Both are true and the reason does not explain the statement.

Answer :

10. Choose the correct statement (multiple may be correct):

• MLE is a special case of MAP when prior is a uniform distribution.
• MLE acts as regularisation for MAP.
• MLE is a special case of MAP when prior is a beta disrubution .
• MAP acts as regularisation for MLE.

Answer : See Answers

NPTEL Introduction to Machine Learning Week 5 Assignment Answers 2024

1. Consider a feedforward neural network that performs regression on a p
-dimensional input to produce a scalar output. It has m hidden layers and each of these layers has k hidden units. What is the total number of trainable parameters in the network? Ignore the bias terms.

• pk+mk²
• pk+mk²+k
• pk+(m−1)k²+k
• p²+(m−1)pk+k
• p²+(m−1)pk+k²

Answer :- c

2.

Answer :- b

3.

Answer :- b, d

4. Which of the following statement(s) about the initialization of neural network weights is/are true?

• Two different initializations of the same network could converge to different minima.
• For a given initialization, gradient descent will converge to the same minima irrespective of the learning rate.
• The weights should be initialized to a constant value.
• The initial values of the weights should be sampled from a probability distribution.

Answer :- a, d

7. Consider a Bernoulli distribution with with p=0.7 (true value of the parameter). We draw samples from this distribution and compute an MAP estimate of p by assuming a prior distribution over p. Which of the following statement(s) is/are true?

• If the prior is Beta(2,6), we will likely require fewer samples for converging to the true value than if the prior is Beta(6,2).
• If the prior is Beta(6,2), we will likely require fewer samples for converging to the true value than if the prior is Beta(2,6).
• With a prior of Beta(2,100), the estimate will never converge to the true value, regardless of the number of samples used.
• With a prior of U(0,0.5)(i.e. uniform distribution between 0 and 0.5), the estimate will never converge to the true value, regardless of the number of samples used.

Answer :- b, d

8. Which of the following statement(s) about parameter estimation techniques is/are true?

• To obtain a distribution over the predicted values for a new data point, we need to compute an integral over the parameter space.
• The MAP estimate of the parameter gives a point prediction for a new data point.
• The MLE of a parameter gives a distribution of predicted values for a new data point.
• We need a point estimate of the parameter to compute a distribution of the predicted values for a new data point.

Answer :- a, b