NPTEL Deep Learning Week 1 Assignment Answers 2025
1.
Answer :- a
2. To measure the Smoothness, coarseness and regularity of a region we use which of the
transformation to extract feature?
a. Gabor Transformation
b. Wavelet Transformation
Both Gabor, and Wavelet Transformation.
d. None of the Above.
Answer :- c
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3. Suppose Fourier descriptor of a shape has K coefficient, and we remove last few coefficient and
use only first m (m<K) number of coefficient to reconstruct the shape. What will be effect of using truncated Fourier descriptor on the reconstructed shape?
a. We will get a smoothed boundary version of the shape.
b. We will get only the fine details of the boundary of the shape.
c. Full shape will be reconstructed without any loss of information.
d. Low frequency component of the boundary will be removed from contour of the shape.
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4. While computing polygonal descriptor of an arbitrary shape using splitting technique, which of
the following we take as the starting guess?
a. Vertex joining the two closet point above a threshold on the boundary.
b. Vertex joining the two farthest point on the boundary.
c. Vertex joining any two arbitrary point on the boundary.
d. None of the above.
Answer :-
5.
Answer :-
6.
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7.
Answer :-
8. Which of the following is not a boundary descriptor.
a. Polygonal Representation
b. Fourier descriptor
c. Signature
d. Histogram.
Answer :-
9. We use gray co-occurrence matrix to extract which type of information?
a. Boundary
b. Texture
c. MFCC
d. Zero Crossing rate.
Answer :-
10. If the larger values of gray co-occurrence matrix are concentrated around the main diagonal, then which one of the following will be true?
a. The value of element difference moment will be low.
b. The value of inverse element difference moment will be low.
c. The value of entropy will be very low.
d. None of the above.
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NPTEL Deep Learning Week 2 Assignment Answers 2025
1. Suppose if you are solving an n-class problem, how many discriminant function you will need
for solving?
a. n-1
b. n
C. n+1
d. n-2
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2. If we choose the discriminant function gi(x) as a function of posterior probability. i.e. gi (x) =
f (p(wi/x)). Then which of following cannot be the function f ()?
a. f(x) = ax, where a > 1
b. f(x) = a-x, where a > 1
c. f(x) = 2x + 3
d. f(x) = exp(x)
Answer :-
3. What will be the nature of decision surface when the covariance matrices of different classes
are identical but otherwise arbitrary? Given all the classes has equal class probabilities)
a. Always orthogonal to two surfaces
b. Generally not orthogonal to two surfaces
c. Bisector of the line joining two mean, but not always orthogonal to two surface.
d. Arbitrary
Answer :-
4.
Answer :-
5. For a two class problem, the linear discriminant function is given by g(x) =aty. What is the
updating rule for finding the weight vector a. Here y is augmented feature vector.
a. Adding the sum of all augmented feature vector which are misclassified multiplied by the
learning rate to the current weigh vector.
b. Subtracting the sum of all augmented feature vector which are misclassified multiplied by
the learning rate from the current weigh vector.
c. Adding the sum of the all augmented feature vector belonging to the positive class
multiplied by the learning rate to the current weigh vector.
d. Subtracting the sum of all augmented feature vector belonging to the negative class
multiplied by the learning rate from the current weigh vector.
Answer :-
6. For minimum distance classifier which of the following must be satisfied?
a. All the classes should have identical covariance matrix and diagonal matrix.
b. All the classes should have identical covariance matrix but otherwise arbitrary.
c. All the classes should have equal class probability.
d. None of above.
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7.
Answer :-
8.
Answer :-
9. In k-nearest neighbour’s algorithm (k-NN), how we classify an unknown object?
a. Assigning the label which is most frequent among the k nearest training samples.
b. Assigning the unknown object to the class of its nearest neighbour among training sample.
C. Assigning the label which is most frequent among the all training samples except the k farthest neighbor.
d. None of this.
Answer :-
10. What is the direction of weight vector w.r.t. decision surface for linear classifier?
a. Parallel
b. Normal
C. At an inclination of 45
d. Arbitrary
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2024 Week 1 Deep Learning Assignment Answers
Q1. Which of the following is (are) region descriptor(s)? Choose the correct option.
i) Fourier descriptor ii) co-occurrence matrix iii) Intensity histogram iv) Signature
a. Both I and IV
b. Only 1
с. Both Il and III
d. None of the above
Answer:- c
Q2.
Answer:- c
Q3.
Answer:- c
Q4.
Answer:- a
Q5.
Answer:- a
Q6.
Answer:- d
Q7. Which of the following is not a Co-occurrence matrix-based descriptor?
a. Entropy
b. Uniformity
c. Intensity histogram.
d. All of the above.
Answer:- c
Q8.
Answer:- d
Q9. What is the value of maximum probability descriptor?
a. 1/4
b. 3/12
c. 1/3
d. 3/16
Answer:- a
Q10. The plot of distance of the different boundary point from the centroid of the shape taken at various direction is known as
a. Signature descriptor
b. Polygonal descriptor
c. Fourier descriptor.
d. Convex Hull
Answer:- a