Are you looking for the Answers to **NPTEL An Introduction to Artificial Intelligence Assignment 9 – IIT Delhi? **This article will help you with the answer to the **Nation**** al Programme on Technology Enhanced Learning (NPTEL)** Course “

**NPTEL An Introduction to Artificial Intelligence**

**Assignment 9**“

## What is An Introduction to Artificial Intelligence?

An Introduction to Artificial Intelligence by **IIT Delhi** course introduces the variety of concepts in the field of artificial intelligence. It discusses the philosophy of AI, and how to model a new problem as an AI problem. It describes a variety of models such as search, logic, Bayes nets, and MDPs, which can be used to model a new problem. It also teaches many first algorithms to solve each formulation. The course prepares a student to take a variety of focused, advanced courses in various subfields of AI.

## CRITERIA TO GET A CERTIFICATE

Average assignment score = 25% of the average of best 8 assignments out of the total 12 assignments given in the course.

Exam score = 75% of the proctored certification exam score out of 100

Final score = Average assignment score + Exam score

**YOU WILL BE ELIGIBLE FOR A CERTIFICATE ONLY IF THE AVERAGE ASSIGNMENT SCORE >=10/25 AND EXAM SCORE >= 30/75. If one of the 2 criteria is not met, you will not get the certificate even if the Final score >= 40/100.**

**Below you can find the answers for NPTEL An Introduction to Artificial Intelligence Assignment 9**

Assignment No. | Answers |
---|---|

Assignment 1An Introduction to Artificial Intelligence | Click Here |

Assignment 2An Introduction to Artificial Intelligence | Click Here |

Assignment 3An Introduction to Artificial Intelligence | Click Here |

Assignment 4An Introduction to Artificial Intelligence | Click Here |

Assignment 5An Introduction to Artificial Intelligence | Click Here |

Assignment 6An Introduction to Artificial Intelligence | Click Here |

Assignment 7An Introduction to Artificial Intelligence | Click Here |

Assignment 8An Introduction to Artificial Intelligence | Click Here |

## NPTEL An Introduction to Artificial Intelligence Assignment 9 Answers:-

**Q1.** Which of the following is true about the MAP (Maximum a posteriori estimate) estimation learning framework?

a. It is equivalent to Maximum Likelihood learning with infinite data

b. It is equivalent to Maximum Likelihood learning if P(θ) is independent of θ

c. it can be used without having any prior knowledge about the parameters

d. The performance of MAP is better with dense data compared to sparse data

Answer:- **a,d**

**Note :- I am not 100% sure about these answers please do it on your own knowledge**

**Q2.** What facts are true about smoothing?

- Smoothed estimates of probabilities fit the evidence better than un-smoothed estimates.
- The process of smoothing can be viewed as imposing a prior distribution over the set of parameters.
- Smoothing allows us to account for data which wasn’t seen in the evidence.
- Smoothing is a form of regularization which prevents overfitting in Bayesian networks.

Answer: **a,c**

**Q3.** Consider three boolean variables X, Y, and Z. Consider the following data:

There can be multiple Bayesian networks that can be used to model such a universe. Assume that we assume a Bayesian Network as shown below:

If the value of the parameter P(¬z|x,¬y) is m/n such that m and n have no common factors. Then, what is the value of m+n? Assume add-one smoothing.

Answer: **343.6**

**Q4.** Consider the following Bayesian Network from which we wish to compute P(x|z) using rejection sampling:

Answer: **86.9**

**Q5.** Assume that we toss a biased coin with heads probability p, 100 times. We get heads 66 times out of 100. If the Maximum Likelihood estimate of the parameter p is m/n where m and n don’t have common factors,

then the value of m+n is?

Answer: **77**

**Q6.** Now, assume that we had a prior distribution over p as shown below:

Answer:- **6.5**

**Q7.** Which of the following task(s) are not suited for a goal based agent?

Answer: **b,c**

**Q8.** Which of the following are true ?

- Rejection sampling is very wasteful when the probability of getting the evidence in the samples is very low.
- We perform conditional probability weighting on the samples while doing Gibbs Sampling in MCMC algorithm since we have already fixed the evidence variables.
- We perform random walk while sampling variables in Likelihood Weighting, MCMC with Gibbs sampling, but not in Rejection sampling.
- Likelihood Weighting functions well if we have many evidence wars with some samples having nearly all the total weight

Answer: **a**

**Q9.** Consider the following Bayesian Network:

- P(C|A,B,D,F,E) = α. P(C|A). P(C|B)
- P(C|A,B,D,F,E) = α. P(C|A,B)
- P(C|A,B,D,F,E) = α. P(C|A,B). P(D|C,E)
- P(C|A,B,D,F,E) = α. P(C|A,B,D,E)

Answer: **b,c**

**Q10.** Which of the following options are correct about the environment of Tic Tac Toe?

- Fully observable
- Stochastic
- Continuous
- Static

Answer: **a,c**

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