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Artificial Intelligence Practice Test Questions: Learning
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Avg score: 72% • Most missed: “Having multiple perceptrons can actually solve the XOR problem satisfactorily:”

Learning in AI programming describes acquiring data and creating rules for how to turn it into actionable information. The rules, which are called algorithms, provide computing devices with step-by-step instructions for how to complete a specific task.
Topics include: Learning, Neural Networks, Decision Trees, & Inductive Logic Programming.

 

Artificial Intelligence Practice Test Questions: Learning
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25 Questions

1. Which method can’t be used for expressing relational knowledge?
2. Which of the following is also called as exploratory learning?
3. What is an auto-associative network?
4. Which of the following does not include different learning methods?
5. Which of the following is an example of active learning?
6. If a hypothesis says it should be positive, but in fact, it is negative, we call it __________
7. The network that involves backward links from output to the input and hidden layers is called _________
8. Which inverts a complete resolution strategy?
9. Why are linearly separable problems of interest of neural network researchers?
10. How many literals are available in top-down inductive learning methods?
11. Which is used for utility functions in game playing algorithm?
12. Which cannot be represented by a set of attributes?
13. Inductive learning involves finding a __________
14. Factors which affect the performance of learner system does not include?
15. What is the name of the function in the following statement “A perceptron adds up all the weighted inputs it receives, and if it exceeds a certain value, it outputs a 1, otherwise it just outputs a 0”?
16. Choose from the following that are Decision Tree nodes?
17. Decision Nodes are represented by ____________
18. What is used in determining the nature of the learning problem?
19. How many types are available in machine learning?
20. Which combines inductive methods with the power of first-order representations?
21. What are the advantages of neural networks over conventional computers?
(i) They have the ability to learn by example
(ii) They are more fault tolerant
(iii)They are more suited for real time operation due to their high ‘computational’ rates
22. Computational learning theory analyzes the sample complexity and computational complexity of __________
23. What is perceptron?
24. Which of the following is true?
Single layer associative neural networks do not have the ability to:
(i) perform pattern recognition
(ii) find the parity of a picture
(iii)determine whether two or more shapes in a picture are connected or not
25. Having multiple perceptrons can actually solve the XOR problem satisfactorily: this is because each perceptron can partition off a linear part of the space itself, and they can then combine their results.

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