NPTEL Deep Learning Week 3 Assignment Answers 2025
1. Find the distance of the 3D point, P = (-3,1, 3) from the plane defined by
2x + 2y + 5z + 9 = 0?
a. 3.1
b. 3.5
C. 0
d. ∞o (infinity)
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2. What is the shape of the loss landscape during optimization of SVM?
a. Linear
b. Paraboloid
c. Ellipsoidal
d. Non-convex with multiple possible local minimum
Answer :-
3. How many local minimum can be encountered while solving the optimization for maximizing
margin for SVM?
a. 1
b. 2
C. ∞o (infinite)
d. 0
Answer :-
4. Which of the following classifiers can be replaced by a linear SVM?
a. Logistic Regression
b. Neural Networks
c. Decision Trees
d. None of the above
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5.
Answer :-
6. For a 2-class problem what is the minimum possible number of support vectors. Assume there
are more than 4 examples from each class?
a. 4
b. 1
C. 2
d. 8
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7. Which one of the following is a valid representation of hinge loss (of margin = 1) for a two-class problem?
y = class label (+1 or -1).
p = predicted (not normalized to denote any probability) value for a class.?
a. L(y, p) = max(0, 1- vp)
b. L(y, p) = min(0, 1- yp)
c. L(y, p) = max(0, 1 + yp)
d. None of the above
Answer :-
8. Suppose we have one feature x € R and binary class y. The dataset consists of 3 points: p1: (x1,
y1) = (-1, -1), p2: (x2, y2) = (1, 1), p3: (x3, y3) = (3, 1). Which of the following true with respect
to SVM?
a. Maximum margin will increase if we remove the point p2 from the training set.
b. Maximum margin will increase if we remove the point p3 from the training set.
c. Maximum margin will remain same if we remove the point p2 from the training set.
d. None of the above.
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9. If we employ SVM to realize two input logic gates, then which of the following will be true?
a. The weight vector for AND gate and OR gate will be same.
b. The margin for AND gate and OR gate will be same.
c. Both the margin and weight vector will be same for AND gate and OR gate.
d. None of the weight vector and margin will be same for AND gate and OR gate.
Answer :-
10. What will happen to the margin length of a max-margin linear SVM if one of non-support vector training example is removed??
a. Margin will be scaled down by the magnitude of that vector
b. Margin will be scaled up by the magnitude of that vector
c. Margin will be unaltered
a. Cannot be determined from the information provided
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