NPTEL Deep Learning – IIT Ropar Week 1 Assignment Answers 2025
1. The points G and C will be classified as?
Note: the notation (G,0) denotes the point G will be classified as class-0 and (C,1) denotes the point C will be classified as class-1
- (C,0),(G,0)
- (C,0),(G,1)
- (C,1),(G,1)
- (C,1),(G,0)
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2. The statement that “there exists more than one decision lines that could separate these data points with zero error” is,
- True
- False
Answer :-
3. Suppose that we multiply the weight vector w by −1. Then the same points G and C will be classified as?
- (C,0),(G,0)
- (C,0),(G,1)
- (C,1),(G,1)
- (C,1),(G,0)
Answer :-
4. Which of the following can be achieved using the perceptron algorithm in machine learning?
- Grouping similar data points into clusters, such as organizing customers based on purchasing behavior.
- Solving optimization problems, such as finding the maximum profit in a business scenario.
- Classifying data, such as determining whether an email is spam or not.
- Finding the shortest path in a graph, such as determining the quickest route between two cities.
Answer :-
5. Consider the following table, where x1 and x2 are features and y is a label.
Assume that the elements in w are initialized to zero and the perception learning algorithm is used to update the weights w. If the learning algorithm runs for long enough iterations, then
- The algorithm never converges
- The algorithm converges (i.e., no further weight updates) after some iterations
- The classification error remains greater than zero
- The classification error becomes zero eventually
Answer :-
6. We know from the lecture that the decision boundary learned by the perceptron is a line in R2. We also observed that it divides the entire space of R2 into two regions, suppose that the input vector x∈R4 , then the perceptron decision boundary will divide the whole R4 space into how many regions?
- It depends on whether the data points are linearly separable or not.
- 3
- 4
- 2
- 5
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7.
- y=1 for (x1,x2,x3) = (0, 0, 0)
- y=0 for (x1,x2,x3) = (0, 0, 1)
- y=1 for (x1,x2,x3) = (1, 0, 0)
- y=1 for (x1,x2,x3) = (1, 1, 1)
- y=0 for (x1,x2,x3) = (1, 0, 1)
Answer :-
8.
Answer :-
9. Which of the following threshold values of MP neuron implements AND Boolean function? Assume that the number of inputs to the neuron is 3 and the neuron does not have any inhibitory inputs.
- 1
- 2
- 3
- 4
- 5
Answer :-
10.
- x1 = −1
- x1 = 1
- x2 = −1
- x2 = 1
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