Are you looking for NPTEL Introduction to Machine Learning Assignment Week 9 Answers? If yes, you will find the answers to the questions asked in the NPTEL Introduction to Machine Learning quiz exam here. If you are preparing for this exam this article will help you in finding the latest and updated answers.

There is a total of 10 questions related to Undirected Graphical Models, HMM, Variable Elimination, Belief Propagation. The correct answers are marked in Green Color with a tick sign.

Note: If the questions in the exam is not same/changed please share them with us, so that we update with the latest questions & answers

NPTEL Introduction to Machine Learning Assignment Week 9 Answers

1. Consider the bayesian network shown below.

Consider the bayesian network shown below.

Two students – Manish and Trisha make the folloWing claims:

  • Manish claims P(D|{S, L, C}) = P(D|{L, C})
  • Trisha claims P(D|{S, L}) = P(D|L)

where P(X|Y) denotes probability of event X given Y . Please note that Y can be a set. Which of the following is true?

  1. Manish and Trisha are correct.
  2. Manish is correct and Trisha is incorrect.
  3. Manish is incorrect and Trisha is correct.
  4. Both are incorrect.
  5. Insufficient information to make any conclusion. Probability distributions of each variable should be given.

2. Consider the same bayesian network shown in previous question (Figure 1). Two other students in the class – Trina and Manish make the following claims:

  • Trina claims P(S|{G, C}) = P(S|C)
  • Manish claims P(L|{D, G}) = P(L|G)

Which of the following is true?

  1. Both the students are correct.
  2. Trina is incorrect and Manish is correct.
  3. Trina is correct and Manish is incorrect.
  4. Both the students are incorrect.
  5. Insufficient information to make any conclusion. Probability distributions of each variable should be given.

3. Consider the Bayesian graph shown below in Figure 2.

Consider the Bayesian graph shown below in Figure 2.

The random variables have the following notation: d – Difficulty, i- Intelligence, g- Grade, s – SAT, l – Letter. The random variables are modeled as discrete variables and the corresponding CPDs are as below.

What is the probabilty of P(i=1,d=0,g=2, s=1,l=1)?

What is the probabilty of P(i=1,d=0,g=2, s=1,l=1)?
  1. 0.004608
  2. 0.006144
  3. 0.001536
  4. 0.003992
  5. 0.009216
  6. 0.007309
  7. None of these

4. Using the data given in the previous question, compute the probability of following assignment, P(i= 1,g= 1, s = 1,l=0) irrespective of the difficulty of the course? (up to 3 decimal places)

  1. 0.160
  2. 0.371
  3. 0.662
  4. 0.047
  5. 0.037
  6. 0.066
  7. 0.189

5. Consider the Bayesian network shown below in Figure 3

Consider the Bayesian network shown below in Figure 3

Two students – Manish and Trisha make the following claims:

  • Manish claims P(H|{S, G, J}) = P(H|{G, J})
  • Trisha claims P(H|{S, C, J}) = P(H|{C, J})

Which of the following is true?

  1. Manish and Trisha are correct.
  2. Both are incorrect.
  3. Manish is incorrect and Trisha is correct.
  4. Manish is correct and Trisha is incorrect.
  5. Insufficient information to make any conclusion. Probability distributions of each variable should be given.

6. Consider the Markov network shown below in Figure 4

Consider the Markov network shown below in Figure 4

Which of the following variables are NOT In the markov blanket of variable “3” shown in the above Figure 4? (multiple answers may be correct)

  1. 1
  2. 8
  3. 2
  4. 5
  5. 6
  6. 4
  7. 7

7. In the Markov network given in Figure 4, two students make the following claims:

  • Manish claims variable “1” is independent of variable “7” given variable “2”
  • Trina claims variable “2” is independent of variable “6” given variable “3”.

Which of the following is true?

  1. Both the students are correct.
  2. Trina is incorrect and Manish is correct.
  3. Trina is correct and Manish is incorrect.
  4. Both the students are incorrect.
  5. Insufficient information to make any conclusion. Probability distributions of each variable should be given.

8. Four random variables are known to follow the given factorization

P(A1 =a1, A2 = a2, A3 = a3, A4 = a4) = 1/Z ψ1 (a2, a32(a1, a43(a2, a44(a1, a3)

The corresponding Markov network would be

  1. The corresponding Markov network would be

9. Does there exist a more compact factorization involving less number of factors for the distribution given in previous question?

  1. Yes
  2. No
  3. Insufficient information

10. Consider the following Markov Random Field.

Consider the following Markov Random Field.
  1. A
  2. B
  3. C
  4. D
  5. E
  6. F
  7. G
  8. H
  9. I
  10. J
NPTEL Introduction to Machine Learning Assignment Answers

FAQ

What is NPTEL Introduction to Machine Learning?

NPTEL Introduction to Machine Learning Course is an online free course by IIT Madras that has been developed by Prof. Balaraman Ravindran. The main aim of this course is to provide the basic concepts of machine learning from a mathematically well-motivated perspective.

Are These Answers Correct?

Yes, all these answers are 100% correct.

Are These Answers Updated?

Yes, these answers are up to date with the latest questions.

Will I Get a Certificate?

Yes, you will get a certificate but it costs ₹1000 exam fee.

Wrap Up

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