### 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)

**Download the 2018 Scheme VTU CBCS Notes and Question Papers of 18CS71 Artificial Intelligence and Machine Learning**

**Follow the link to download the 2018 Scheme VTU CBCS Notes**

**Download the 2018 Scheme 7th Semester VTU Question Papers**

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

### Summary

Here you can find the Solution to the 18CS71 Artificial Intelligence and Machine Learning (AIML) Model Question Paper. If you like the material share it with your friends. Like the **Facebook page** for regular updates and **YouTube channel** for video tutorials.