1. 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 :- b, d

2. In the perceptron model, the weight w vector is perpendicular to the linear decision boundary at all times.

• True
• False

Answer :- a

3. What is the perceptron algorithm used for?

• Clustering data points
• Classifying data
• Solving optimization problems
• Finding the shortest path in a graph

Answer :- b

Answer :- b, d, e

5. Which of the following Boolean functions can be implemented using a perceptron?

• NOR
• NAND
• NOT
• XOR

Answer :- a, b, c

6. Which of the following threshold values of MP neuron implements AND Boolean function? Assume that the number of inputs to the neuron is 7 and the neuron does not have any inhibitory inputs.

• 1
• 3
• 6
• 7
• 8

Answer :- d

7. Suppose we have a boolean function that takes 4 inputs x₁,x₂,x₃,x₄? We have an MP neuron with parameter θ=3. For how many inputs will this MP neuron give output y=1?

• 5
• 4
• 1
• 16

Answer :- a

8.

• x1=−1
• x1=1
• x2=−1
• x2=1

Answer :- a, d

Answer :- c

10. Which Boolean function with two inputs x1 and x2 is represented by the following decision boundary? (Points on boundary or right of the decision boundary to be classified 1)

• AND
• OR
• XOR
• NAND

Answer :- b

11.

Answer :- a, b, c, d

12. Suppose we have a boolean function that takes 4 inputs x1, x2, x3, x4? We have an MP neuron with parameter θ=2. For how many inputs will this MP neuron give output y=1?

• 11
• 21
• 15
• 8

Answer :- a

13. We are given the following data:

Can you classify every label correctly by training a perceptron algorithm? (assume bias to be 0 while training)

• Yes
• No

Answer :- b

14. We are given the following dataset with features as (x1,x2) and y as the label (-1,1). If we apply the perception algorithm on the following dataset with w initialized as (0,0). What will be the value of w when the algorithm converges? (Start the algorithm from (2,2)

• (-2,2)
• (2,1)
• (2,-1)
• None of These

Answer :- c

15. Consider points shown in the picture. The vector w is (-1,0). As per this weight vector, the Perceptron algorithm will predict which classes for the data points x1 and x2.

• x1=1
• x2=1
• x1=-1
• x2=-1

Answer :- b, c

16. Given an MP neuron with the inputs as x1,x2,x3,x4,x5 and threshold θ=3 where x5 is inhibitory input. For input (1,1,1,0,1) what will be the value of y?

• y=0
• y=1since θ≥3
• y=1/2
• Insufficient information

Answer :- a

17. An MP neuron takes two inputs x1 and x2. Its threshold is θ=0. Select all the boolean functions this MP neuron may represent.

• AND
• NOT
• OR
• NOR

Answer :- d

18. What is the output of a perceptron with weight vector w=[2 −3 1] and bias b=−2 when the input is x=[10−1]?

• 0
• 1
• -1
• 2

Answer :- c

19. What is the ”winter of AI” referring to in the history of artificial intelligence?

• The period during winter when AI technologies are least effective due to cold temperatures
• A phase marked by decreased funding and interest in AI research.
• The season when AI algorithms perform at their peak efficiency.
• A period characterized by rapid advancements and breakthroughs in AI technologies

Answer :- b