NPTEL An Introduction to Artificial Intelligence Week 9 Assignment Answers 2024

Delve into NPTEL's Week 9 Assignment for Insights into Artificial Intelligence

Artificial Intelligence (AI) enthusiasts, buckle up! NPTEL's Week 9 Assignment is here to test your mettle and deepen your understanding of this cutting-edge field. Let's embark on a journey through the intricacies of AI, unraveling the mysteries one assignment at a time.

Deciphering NPTEL's Week 9 Assignment

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There is a biased coin with probability of heads as θ. Let the prior on this probability be given by the PDF P(θ) = 6θ (1- θ). The coin is flipped 5 times, and it lands up on heads each time. The MAP estimate of θ is:

Answer: 0.833

How many of the following statements are CORRECT?

a. Rejection sampling (before rejection step) samples from the posterior distribution of the Bayes Net given the evidence variables

b. In the limit of infinite sampling, rejection sampling will return consistent posterior estimates

c. In the limit of infinite sampling, likelihood weighting will return consistent posterior estimates

d. Exact Inference in Bayesian networks can be done in Polynomial time in the worst case.

e. MCMC with Gibbs Sampling is guaranteed to converge to the same distribution irrespective of the initial state for all kinds of Bayes Networks

f. Performance of Likelihood weighting degrades in practice when there are many evidence variables that are lower in the Bayes Net

g. Late occurring evidence variables can help guide sample generation in likelihood weighting

Answer: 3

What is the P(?1 = 1) + P(?5 = 2), where “?i” indicates “?” in the ith row, after the 1st E-step. (round up to 2 decimal places)

Answer: For Answer Click Here

What is P(X = 1 | B = 1) – P(B = 0 | A = 1) after the 1st M-step? (round up to 3 decimal places)

Answer:

Consider the bayes-net...

NPTEL An Introduction to Artificial Intelligence Week 9 Assignment Answers 2024

We wish to calculate P(c | ¬w, r). Select the correct statements –

When using rejection sampling, we will accept the sample ( ¬c, s, ¬w, r).

When using likelihood weighting, every sample will be weighted 0.5

When using MCMC with Gibbs sampling, we vary assignments to C and S.

S is conditionally independent of all other variables, given its parent C.

Answer:

Which of the following is/are INCORRECT?

ML (Maximum Likelihood) works better than MAP when the data is sparse.

In the limit of infinite data, MAP reduces to MLE.

MAP estimation needs a prior distribution on the parameter values

MLE assumes a beta distribution prior on the parameter values

Answer: For Answer Click Here

In this segment, we'll decode the enigmatic questions posed in NPTEL's Week 9 Assignment. Prepare to engage your cognitive faculties as we explore the realms of AI with precision and clarity.

Understanding Artificial Intelligence Concepts

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In Conclusion

NPTEL's Week 9 Assignment serves as a beacon for AI enthusiasts worldwide, illuminating the path to deeper understanding and mastery. Embrace the challenges posed, seize the opportunity to expand your knowledge, and let your passion for artificial intelligence propel you toward greater heights