Are you looking for NPTEL Introduction to Machine Learning Assignment Week 1 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 Introduction: Statistical Decision Theory – Regression, Classification, Bias Variance. The correct answers are marked in Green Color with a tick sign.

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NPTEL Introduction to Machine Learning Assignment Week 1 Answers

1. Which of the following is a supervised learning problem?

1. Grouping people in a social network.
2. Predicting credit approval based on historical data
3. Predicting rainfall based on historical
4. All of the above

2. Which of the following is not a classification problem?

1. Predicting the temperature (in Celsius) of a room from other environmental features (such as atmospheric pressure, humidity etc).
2. Predicting if a cricket player is a batsman or bowler given his playing records.
3. Predicting if a particular route between two points has traffic jam or not based on the travel time of vehicles.
4. Filtering of spam messages

3. Which of the following is a regression task? (multiple options may be correct)

1. Predicting the monthly sales of a cloth store in rupees.
2. Predicting if a user would like to listen to a newly released song or not based on historical data.
3. Predicting the confirmation probability (in fraction) of your train ticket whose current status is waiting list based on historical data.
4. Predicting if a patient has diabetes or not based on historical medical records.
5. Predicting the gender of a human based on facial features.

4. Which of the following is an unsupervised task?

1. Learning to play chess.
2. Predicting if a new edible item is sweet or spicy based on the information of the ingredients, their quantities, and labels (sweet or spicy) for many other similar dishes.
3. Grouping related documents from an unannotated corpus.
4. all of the above

5. Which of the following is a categorical feature?

1. Number of legs of an animal
2. Number of hours you study in a day
3. Your weekly expenditure in rupees.
4. Branch of an engineering student
5. Ethnicity of a person
6. Height of a person in inches

6. Let X and Y be a uniformly distributed random variable over the interval [0, 4] and [0, 6] respectively. If X and Y are independent events, then compute the probability, P(max(X,Y)>3)

1. 1/6
2. 5/6
3. 2/3
4. None of the above

7. Let the trace and determinant of a matrix A[acbd] be 6 and 16 respectively. The eigenvalues of A are.

1. 3+ı√7/2, 3-ı√7/2 where ı=√-1
2. 1, 3
3. None of the above
4. Can be computed only if A is a symmetric matrix.
5. Can not be computed as the entries of the matrix A are not given.

8. What happens when your model complexity increases? (multiple options may be correct)

1. Model Bias decreases
2. Model Bias increases
3. Variance of the model decreases
4. Variance of the model increases

9. A new phone, E-Corp X1 has been announced and it is what you’ve been waiting for, all along. You decide to read the reviews before buying it. From past experiences, you’ve figured out that good reviews mean that the product is good 90% of the time and bad reviews mean that it is bad 70% of the time. Upon glancing through the reviews section, you find out that the X1 has been reviewed 1269 times and only 172 of them were bad reviews. What is the probability that, if you order the X1, it is a bad phone?

1. 0.32
2. 0.2
3. 0.44
4. 0.56

10. Which of the following are false about bias and variance of overfitted and underfitted models? (multiple options may be correct)

1. Underfitted models have high bias.
2. Underfitted models have low bias.
3. Overfitted models have low variance.
4. Overfitted models have high variance.
5. None of these

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