BSc CS Sem 5 BSc CS Semester 5 (2018 2019) 2019 Artificial Intelligence Question Paper - Mumbai University | munotes

Questions asked in this paper

• 2) Figures to the right indicate marks

1. Q3 Illustrations, in-depth answers and diagrams will be appreciated
2. Q4 Mixing of sub-questions is not allowed
3. Q1 Attempt All (Each of 5Marks) 15 marks
   • (a) 1. is not of node structure
   • a) state b)Parent
   • c) child d) Action
4. Q2 is also called as Heuristic search
5. Q3 agent does not maintain internal state
6. Q4 If a hypothesis agrees with all the data, it is called as
7. Q5 The most widely used ensemble method is called
   • (b) Fill in the blanks
8. Q2 is finding the numerical parameters for a probability model whose structure is fixed
9. Q3 This process of explicitly penalizing complex hypothesis is
10. Q4 agent knows the actual outcome of its actions and can act
11. Q5 consists of series of tests, each of which is a conjunction of
    • (c) Short Answers(Unit-I, IT and ITI)
12. Q1 What is early stopping?
13. Q2 Define Error Rate
14. Q3 How denote learning rate?
15. Q4 Define decision boundary
16. Q5 What is triangle inequality?
17. Q2 Attempt the following (Any THREE)(Each of 5Marks) 15 marks
    • (a) Describe Model-based agent
    • (b) What is PEAS? Mention it for Part picking robot and Medical Diagnosis
    • (c) Explain Artificial Intelligence with Turing Test approach
    • (d) Describe problem formulation of vacuum world problem
    • (e) Explain these properties of task environment
    • (f) List and explain the categories of definition of AI
18. Q3 Attempt the following (Any THREE) (Each of 5Marks) 15 marks
    • (a) Explain the concept of Locality Sensitive Hashing
    • (b) Write a note on Artificial Neural Network
    • (c) Explain K-fold cross validation and LOOCV
    • (d) Write a note on Supervised Learning
    • (e) What is entropy? How do we calculate it?
    • (f) Write a note on Nearest Neighbor model
19. Q4 Attempt the following (Any THREE) (Each of 5Marks) 15 marks
    • (a) Explain the concept of Passive Reinforcement Learning
    • (b) Write a note on Statistical Learning
    • (c) Explain Hidden Markov Model
    • (d) Briefly explain the concept of direct utility estimation
    • (e) What are the applications of Reinforcement Learning?
    • (f) Explain the concept of EM algorithm
20. Q5 Attempt the following (Any THREE) (Each of 5Marks) 15 marks
    • (a) Explain Breadth First Search strategy along with its pseudocode
    • (b) Write a note on Decision Tree. Also describe its pruning technique
    • (c) Explain Naive Bayes Model
    • (d) Explain the concept of Goal Based Agent
    • (e) Write a note on overfitting in decision tree

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