# ML Deep Learning Fundamentals Applications Week 2 Answers

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**Course Name: Machine Learning and Deep Learning – Fundamentals and Applications**

## Table of Contents

## ML Deep Learning Fundamentals Applications Week 2 Answers (July-Dec 2024)

**Q1.Consider a binary classification problem with two classes, A and B with prior probability P(A)=0.6and P(B)=0.4 .Let X be a single binary feature that can take values 0 or 1 .Given: P(X=1|A)=0.8and P(X=0|B)=0.7.Determine which class the classifier will classify when X=1**

Class A

Class B

Equiprobable for Class A and Class B

Not enough information

**Answer: Class A**

**Q2. Consider the following Bayesian network, where F = having the flu and C = coughing:**

0.23

0.03

0.35

None of the above.

**Answer: 0.23**

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**These are ML Deep Learning Fundamentals Applications Week 2 Answers**

**Q3. For the above question, Are C and F independent in the given Bayesian network?**

Yes.

No.

Can’t say.

Insufficient information.

**Answer: No.**

**Q4. Bayes’ decision theory assumes that:**

The feature vectors are dependent on each other.

The feature vectors are normally distributed.

The feature vectors are identically distributed.

The feature vectors are uniformly distributed.

**Answer: The feature vectors are identically distributed.**

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**These are ML Deep Learning Fundamentals Applications Week 2 Answers**

**Q5. Assume that the word ‘offer’ occurs in 80% of the spam messages in my account. Also, let’s assume ‘offer’ occurs in 10% of my desired e-mails. If 30% of the received e-mails are considered as a scam, and I will receive a new message which contains ‘offer’, what is the probability that it is spam?**

0.778

0.774

0.668

0.664

**Answer: 0.774**

**Q6. The optimal decision in Bayes Decision Theory is the one that**

Minimizes the error rate.

Maximizes the error rate.

Minimizes the loss function.

Maximizes the loss function.

Answer: Minimizes the loss function.

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**These are ML Deep Learning Fundamentals Applications Week 2 Answerss**

**Q7. The risk function in Bayesian decision theory combines:**

The prior probabilities and the likelihood function.

The decision boundaries and the feature vectors.

The training set and the test set.

The loss function and the decision rule

**Answer: The loss function and the decision rule**

**Q8. The loss function used in risk-based Bayesian decision theory:**Quantifies the cost of different types of errors.

Is equal to the likelihood function.

Ignores the prior probabilities of the classes.

Is not used in the decision-making process

**Answer: Quantifies the cost of different types of errors.**

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**These are Machine Learning and Deep Learning Fundamentals and Applications Week 1 Nptel Assignment Answers**

**Q9. The risk-based Bayesian decision rule accounts for the consequences of different decisions by considering the:**

Number of features in the dataset

The complexity of the classifier

Uncertainty in the data and the associated losses

Mean and standard deviation of the feature vectors

**Answer: Uncertainty in the data and the associated losses**

**Q10. The generalized form of a Bayesian network that represents and solves decision problems under uncertain knowledge is known as an?**

Directed Acyclic Graph

Table of conditional probabilities

Influence diagram

None of the above

**Answer: Influence diagram**

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**These are Machine Learning and Deep Learning Fundamentals and Applications Week 2 Nptel Assignment Answers**

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