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### Solution to 18CS71 Artificial Intelligence and Machine Learning (AIML) Model Question Paper

**MODULE-1**

**b. Stat**e and **explain the **algorithm for Best First Search Algorithm with an example. (10 Marks)

### OR

- Write down the production rules for the above problem
- Write any one solution to the above problem (10 Marks)

b. **Elaborate on **the steps of Simulated Annealing. (10 Marks)

## MODULE-2

3. a. **Describe **the issues of Knowledge Representation (10 Marks)

b. Consider the following set of well-formed formulas in predicate logic

- Man(Marcus)
- Pompeian(Marcus)
- ∀x: Pompeian(x) → Roman(x)
- ruler(Caesar)
- ∀x: Roman(x) → loyalto(X. Caesar) V hate(x, Caesar)
- ∀x :→y: loyalto(x,y)
- ∀x :∀ y : man(x) ∧ ruler(y) ∧ tryassassinate(x,y) → loyalto(x,y)
- tryassassinate (Marcus, Caesar)

Convert these into clause form and prove that hate (Marcus, Caesar) using resolution proof (10 Marks)

### OR

4. a. **Recall **Concept Learning and also Explain hypothesis space of Find-S (05 Marks)

Ex. | Sky | Airtemp | Humidity | wind | Water | Forecast | Enjoy |

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 |

c. **Compare **the key differences between Find-S and Candidate Elimination Algorithm. (05 Marks)

### MODULE-3

5. a. **Outline **the ID3 Decision Tree Learning method. (08 Marks) – v

**c. Construct **Decision trees to represent the following Boolean functions

A and B

A or [B and C]

[A and B] or [C and D] (04 Marks)

### OR

6. a. For the transactions shown in the table compute the following: – v

- Entropy of the collection of transaction records of the table with respect to classification.
- What is the information gain of A1 and A2 relative to the transactions of the table? (08 Marks)

Instance. | Classification | A1 | A2 |

1 | + | T | T |

2 | + | T | T |

3 | – | T | F |

4 | + | F | F |

5 | – | F | T |

6 | – | F | T |

b. **Discuss **the application of Neural Network which is used for learning to steer an autonomous vehicle. (06 Marks)

## MODULE-4

**7. a. Illustrate **Bayes Theorem and maximum posterior hypothesis. (06 Marks) – v

Color | Type | Origin | Stolen |

Red | Sports | Domestic | Yes |

Red | Sports | Imported | Yes |

Red | SUV | Imported | No |

Yellow | Sports | Domestic | No |

Yellow | SUV | Imported | Yes |

Yellow | Sports | Domestic | Yes |

Red | SUV | Imported | No |

**c. Outline **Brute force MAP Learning Algorithm. (06 Marks)

### OR

8. a. **Demonstrate **the derivation of the K-Means Algorithm. (10 Marks)

b. Bring out the steps of the Gibbs Algorithm. (04 Marks)

c. **Discuss **the Minimum Description Length algorithm. (06 Marks)

## MODULE-5

9. a. With a neat sketch briefly explain Global Approximation of Radial basis Function. (10 Marks)

### OR

BMI | Age | Sugar |

33.6 | 50 | 1 |

26.6 | 30 | O |

23.4 | 40 | O |

43.1 | 67 | O |

35.3 | 23 | 1 |

35.9 | 67 | 1 |

36.7 | 45 | 1 |

25.7 | 46 | O |

23.3 | 29 | O |

31 | 56 | 1 |

Assume K=3, Test Example is BMI=43.6, Age=40, Sugar=? (10 Marks)

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## 18CS71 Artificial Intelligence and Machine Learning Module wise Question Bank with Solutions

### Summary

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