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)=0.6
and P(B)=0.4 .Let X be a single binary feature that can take values 0 or 1 .Given: P(X=1|A)=0.8
and 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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