NPTEL Data Mining Week 6 Assignment Answers 2025
1. A perceptron consists of –
A. one neuron
B. two neuron
C. three neuron
D. four neuron
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2. A perceptron can correctly classify instances into two classes where the classes are:
A. Overlapping
B. Linearly separable
C. Non-linearly separable
D. None of the above
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3. The logic function that cannot be implemented by a perceptron having two inputs is?
A. AND
B. OR
C. NOR
D. XOR
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4. A training input x is used for a perceptron learning rule. The desired output is t and the actual output is o. If learning rate is η, the weight update performed by the learning rule is describedby?
A. wi←wi+ h(t – o)
B. wi←wi+ h(t – o) x
C. wi←h(t – o) x
D. wi←wi+ (t – o) x
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5.
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6.
A. Discontinuous and not differentiable
B. Discontinuous but differentiable
C. Continuous but not differentiable
D. Continuous and differentiable
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7. The neural network given bellow takes two binary valued inputs x1,x2ϵ{0,1} and the activation function is the binary threshold function h(z)=1 if z>0; 0 otherwise
Which of the following logical functions does it compute?
A. OR
B. AND
C. NAND
D. NOR
Answer :-
8. The neural network given bellow takes two binary valued inputs x1,x2ϵ{0,1}
and the activation function is the binary threshold function h(z)=1if z>0; 0 otherwise. Which of the following logical functions does it compute?
A. OR
B. AND
C. NAND
D. NOR
Answer :-
9. Which of the following statement is true for a multilayered perceptron?
A. Output of all the nodes of a layer is input to all the nodes of the next layer
B. Output of all the nodes of a layer is input to all the nodes of the same layer
C. Output of all the nodes of a layer is input to all the nodes of the previous layer
D. Output of all the nodes of a layer is input to all the nodes of the output layer
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10. A multi-layered perceptron is usually trained using:
A. margin maximization algorithm
B. single linkage algorithm
C. belief propagation algorithm
D. backpropagation algorithm
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11. Overfitting is expected when we observe that?
A. With training iterations error on training set as well as test set decreases
B. With training iterations error on training set decreases but test set increases
C. With training iterations error on training set as well as test set increases
D. With training iterations training set as well as test set error remains constant
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