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Artificial Intelligence Practice Test Questions: Learning
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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. What is Decision Tree?
2. The network that involves backward links from output to the input and hidden layers is called _________
3. Which of the following is true?
(i) On average, neural networks have higher computational rates than conventional computers.
(ii) Neural networks learn by example.
(iii) Neural networks mimic the way the human brain works.
4. How is Fuzzy Logic different from conventional control methods?
5. Which is true for neural networks?
6. Which of the following are the advantage/s of Decision Trees?
7. A _________ is a decision support tool that uses a tree-like graph or model of decisions and their possible consequences, including chance event outcomes, resource costs, and utility.
8. 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”?
9. Neural Networks are complex ______________with many parameters.
10. Which of the following statement is true?
11. What need to be satisfied in inductive logic programming?
12. What is Neuro software?
13. Which produces hypotheses that are easy to read for humans?
14. In which of the following learning the teacher returns reward and punishment to learner?
15. What is an auto-associative network?
16. A perceptron is a ______________
17. What is used in determining the nature of the learning problem?
18. Which is used to choose among multiple consistent hypotheses?
19. Why are linearly separable problems of interest of neural network researchers?
20. What will happen if the hypothesis space contains the true function?
21. 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.
22. Which cannot be represented by a set of attributes?
23. How many literals are available in top-down inductive learning methods?
24. Which combines inductive methods with the power of first-order representations?
25. Neural Networks are complex ______________ with many parameters.

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