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

Here you can find the module-wise questions and solutions for 18CS71 Artificial Intelligence and Machine Learning.

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

## Module 1 – Introduction to Artificial Intelligence

1. What is Artificial Intelligence? List the fields that form the basis for AI.

2. List the task domains of AI. Explain why artificial intelligence is important?

3. What is Artificial Intelligence? List four things (steps) to build a system to solve a problem.

5. What is a Production System? List and explain production system classes or categories.

6. Explain the water Jug problem as a state-space search.

7. Explain problem characteristics in artificial Intelligence.

8. Define Control Strategy and requirements for good search strategy.

9. Define State Space Search. Explain in detail.

10. How do you define a problem as a state-space search, Explain.

11. Explain Breadth-first search (BFS) with an example. List down the advantages and disadvantages.

12. Explain Depth-first search (DFS) with an example. List down the advantages and disadvantages.

13. What do you mean by heuristic and heuristic search? What are the advantages of Heuristic Search?

14. Write and explain Generate and Test algorithm.

16. Consider trying to solve the 8-puzzle instance is given below using Hill Climbing. Apply any heuristic function appropriate to the problem.

**Goal State**

1 | 2 | 3 |

8 | 4 | |

7 | 6 | 5 |

17. What is the constraint satisfaction problem? Explain it.

18. Write and explain the Best first search algorithm with an example.

19. Write and explain the A* algorithm with advantages and disadvantages.

20. Explain Problem reduction techniques with a suitable example.

22. What is Means-Ends Analysis? Explain with an example.

23. A star Search Algorithm to Move from the start state to the final state 8 Puzzle Problem.

24. A* star Search Algorithm to move from start node A to final node J.

## Module 2 – 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 different perspectives and issues in machine learning.

4. Define concept learning and discuss it with an example.

5. Explain the General-to-Specific Ordering of Hypotheses

6. Write the FIND-S algorithm and explain with an 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 |

7. What are the key properties and complaints of the FIND-S algorithm?

8. Define Consistent Hypothesis and Version Space.

9. Write LIST-THEN-ELIMINATE algorithm.

10. Write the candidate elimination algorithm and illustrate with an example.

11. Write the final version space for the below-mentioned training examples using the candidate elimination algorithm.

Example | Shape | Size | Color | Surface | Thickness | Target Concept |

1 | Circular | Large | Light | Smooth | Thick | Malignant (+) |

2 | Circular | Large | Light | Irregular | Thick | Malignant (+) |

3 | Oval | Large | Dark | Smooth | Thin | Benign (-) |

4 | Oval | Large | Light | Irregular | Thick | Malignant (+) |

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 | 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 |

Ex | Eyes | Nose | Head | Fcolor | Hair | Smile |

1 | Round | Triangle | Round | Purple | Yes | Yes |

2 | Square | Square | Square | Green | Yes | No |

3 | Square | Triangle | Round | Yellow | Yes | Yes |

4 | Round | Triangle | Round | Green | No | No |

5 | Square | Square | Round | Yellow | Yes | Yes |

**Example – 5: Solution**

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 |

12. Explain in detail the Inductive Bias of Candidate Elimination algorithm.

13. Explain FIND-S Algorithm Unanswered Questions.

## Module 3 – Decision Trees and Artificial Neural Networks

2. What are appropriate problems for Decision tree learning?

3. Explain the concepts of Entropy and Information gain.

4. Describe the ID3 algorithm for decision tree learning with an example.

5. Give Decision trees to represent the Boolean Functions: – v

6. Give Decision trees for the following set of training examples – v

Day | Outlook | Temperature | Humidity | Wind | play tennis |

D1 | Sunny | Hot | High | Weak | No |

D2 | Sunny | Hot | High | Strong | No |

D3 | Overcast | Hot | High | Weak | Yes |

D4 | Rain | Mild | High | Weak | Yes |

D5 | Rain | Cool | Normal | Weak | Yes |

D6 | Rain | Cool | Normal | Strong | No |

D7 | Overcast | Cool | Normal | Strong | Yes |

D8 | Sunny | Mild | High | Weak | No |

D9 | Sunny | Cool | Normal | Weak | Yes |

D10 | Rain | Mild | Normal | Weak | Yes |

D11 | Sunny | Mild | Normal | Strong | Yes |

D12 | Overcast | Mild | High | Strong | Yes |

D13 | Overcast | Hot | Normal | Weak | Yes |

D14 | Rain | Mild | High | Strong | No |

7. Consider the following set of training examples. – v

- What is the entropy of this collection of training examples with respect to the target function classification?
- What is the information gain of a
_{2}relative to these training examples?

Instance | Classification | a_{1} | a_{2} |

1 | + | T | T |

2 | + | T | T |

3 | – | T | F |

4 | + | F | F |

5 | – | F | T |

6 | – | F | T |

8. Identify the entropy, information gain and draw the decision trees for the following set of training examples

Gender | Car ownership | Travel cost | Income Level | Transportation (Class) |

Male | 0 | Cheap | Low | Bus |

Male | 1 | Cheap | Medium | Bus |

Female | 1 | Cheap | Medium | Train |

Female | 0 | Cheap | Low | Bus |

Male | 1 | Cheap | Medium | Bus |

Male | 0 | Standard | Medium | Train |

Female | 1 | Standard | Medium | Train |

Female | 1 | Expensive | High | Car |

Male | 2 | Expensive | Medium | Car |

Female | 2 | Expensive | High | Car |

Instance | a1 | a2 | a3 | Classification |

1 | True | Hot | High | No |

2 | True | Hot | High | No |

3 | False | Hot | High | Yes |

4 | False | Cool | Normal | Yes |

5 | False | Cool | Normal | Yes |

6 | True | Cool | High | No |

7 | True | Hot | High | No |

8 | True | Hot | Normal | Yes |

9 | False | Cool | Normal | Yes |

10 | False | Cool | High | Yes |

10. Discuss Hypothesis Space Search in Decision tree Learning.

11. List and explain issues in learning decision trees.

13. Explain the single perceptron with its learning algorithm.

14. How a single perceptron can be used to represent the Boolean functions such as AND, OR gate.

AND GATE Solution, OR GATE Solution

15. Write Gradient Descent algorithm for training a linear unit. Also, Derive the Gradient Descent Rule

16. Write Stochastic Gradient Descent algorithm for training a linear unit. Differentiate between Gradient Descent and Stochastic Gradient Descent

18. Derive the Back Propagation Rule for artificial neural networks.

## Module 4 – Baysian Learning

1. Define the Bayesian theorem? What are the relevance and features of the Bayesian theorem? Explain the practical difficulties of the Bayesian theorem.

3. Consider a medical diagnosis problem in which there are two alternative hypotheses: 1. that the patient has a particular form of cancer (+) and 2. That the patient does not (-). A patient takes a lab test and the result comes back positive. The test returns a correct positive result in only 98% of the cases in which the disease is actually present, and a correct negative result in only 97% of the cases in which the disease is not present. Furthermore, .008 of the entire population have this cancer. Determine whether the patient has Cancer or not using MAP hypothesis.

4. Explain Brute force Bayes Concept Learning. **View Solution**

5. What are Consistent Learners? Discuss Maximum Likelihood and Least Square Error Hypothesis. **View Solution**

6. Describe the Maximum Likelihood Hypothesis for predicting probabilities.

7. Describe the concept of MDL. Obtain the equation for h_{MDL}.

8. Explain Naïve Bayes Classifier with an Example. **View Solution**

9. What are Bayesian Belief nets? Where are they used?

10. Explain the Bayesian belief network and conditional independence with an example.

## Module 5 – Instance Based Learning, Reinforcement Learning

1. What is instance-based learning? Explain the key features and disadvantages of these methods. – **View Solution**

2. Explain the K – nearest neighbor algorithm for approximating a discrete-valued target function with pseudo code. Also, explain the distance weighted KNN algorithm for the discrete-valued target function. – **View Solution**

3. Describe K-nearest Neighbour learning Algorithm for the continuous (real) valued target function. Also, explain the distance weighted KNN algorithm for the real-valued target function. – **View Solution**

4. Discuss the major drawbacks of the K-nearest Neighbour learning Algorithm and how it can be corrected.

5. Explain Locally Weighted Linear Regression. – **View Solution**

6. Explain radial basis function

7. Explain the CADET System using Case-based reasoning.

8. What is Reinforcement Learning and explain the Reinforcement learning problem with a neat diagram? – **View Solution**

9. Write Reinforcement learning problem characteristics.

10. Explain the Q function and Q Learning Algorithm assuming deterministic rewards and actions with example. – **View Solution**

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