17CS73 Machine Learning Question With Answers Module-1
MODULE 1 – INTRODUCTION AND CONCEPT LEARNING
1. Define Machine Learning. Explain with examples why machine learning is important.
2. Discuss some applications of machine learning with examples.
3. Explain how some disciplines have influenced machine learning.
4. What is well-posed learning problems.
5. Describe the following problems with respect to Tasks, Performance, and Experience:
- A Checkers learning problem
- A Handwritten recognition learning problem
- A Robot driving learning problem
6. Explain the steps in designing learning systems in detail.
7. Explain different perspectives and issues in machine learning.
8. Define concept learning and discuss it with an example.
9. Explain the General-to-Specific Ordering of Hypotheses
10. Write the FIND-S algorithm and explain with the example given below
| Example | Sky | AirTemp | Humidity | Wind | Water | Forecast | EnjoySport |
| 1 | Sunny | Warm | Normal | Strong | Warm | Same | Yes |
| 2 | Sunny | Warm | High | Strong | Warm | Same | Yes |
| 3 | Rainy | Cold | High | Strong | Warm | Change | No |
| 4 | Sunny | Warm | High | Strong | Cool | Change | Yes |
11. What are the key properties and complaints of the FIND-S algorithm?
12. Define Consistent Hypothesis and Version Space.
13. Write LIST-THEN-ELIMINATE algorithm. Give Example.
14. Write the candidate elimination algorithm and illustrate with an example
15. Write the final version space for the below-mentioned training examples using the candidate elimination algorithm.
Example – 1:
| Origin | Manufacturer | Color | Decade | Type | Example Type |
| Japan | Honda | Blue | 1980 | Economy | Positive |
| Japan | Toyota | Green | 1970 | Sports | Negative |
| Japan | Toyota | Blue | 1990 | Economy | Positive |
| USA | Chrysler | Red | 1980 | Economy | Negative |
| Japan | Honda | White | 1980 | Economy | Positive |
| Japan | Toyota | Green | 1980 | Economy | Positive |
| Japan | Honda | Red | 1990 | Economy | Negative |
Example – 2:
| Size | Color | Shape | Class |
| Big | Red | Circle | No |
| Small | Red | Triangle | No |
| Small | Red | Circle | Yes |
| Big | Blue | Circle | No |
| Small | Blue | Circle | Yes |
Example – 3:
| Example | Citations | Size | InLibrary | Price | Editions | Buy |
| 1 | Some | Small | No | Affordable | One | No |
| 2 | Many | Big | No | Expensive | Many | Yes |
| 3 | Many | Medium | No | Expensive | Few | Yes |
| 4 | Many | Small | No | Affordable | Many | Yes |
16. Explain in detail the Inductive Bias of Candidate Elimination algorithm.
17. Explain FIND-S Algorithm Unanswered Questions.
Summary:
Here you find the Machine Learning Question With Answers Module 1.