BSc CS Sem 5 BSc CS Semester 5 (2019 2020) Oct 2020 ARTIFICIAL INTELLIGENCE 16.10.19 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) Multiple Choice Questions
4. Q1 .-environment gives access to agent’s sensor to the complete set
   • c) deterministic d) Episodic
   • ii) The set of all leaf nodes available for expansion at any given point is
   • a) Frontier b) Backtier
   • c) Depth d) Width ill) .expands the shallowest unexpanded node first
   • c) IDA d) A
5. Q4 has connections only in one direction
   • v) Automated Vehicle is an example
   • (b) Fill in the blanks
6. Q1 strategy is called as blind search
   • i) algorithm is used for solving temporal probabilistic expands node with minimal f(n)= g(n)+h(n)
   • iv) is time complexity of Bidirectional Search
   • v) Classification is task
   • (c) Short Answers
   • i) Define deterministic task environment List the parameters used to evaluate performance of Search algorithms
   • iii) What is supervised learning?
   • iv) What are the examples of nonparametric model?
   • v) What is maximum-likelihood learning?
7. Q2 Attempt the following (Any THREE)(Each of 5Marks) 15 marks
   • (a) Write states, Initial States, Actions, Transition Model and Goal test to formulate
   • (b) Describe Utility based agent
   • (c) Describe general Graph-search algorithm
   • (d) Explain Thinking rationally and Acting rationally approaches of AI
   • (e) What is PEAS? Describe it for Satellite image analysis system and Interactive
   • (f) Explain following task environment
8. Q3 Attempt the following (Any THREE) (Each of 5Marks) 15 marks
   • (a) Describe Linear classifiers with hard threshold
   • (b) Explain Single-layer feed forward neural networks
   • (c) Explain the Restaurant wait problem with respect to decision trees representation
   • (d) Describe K-fold cross validation and LOOCV
   • (e) Describe Univariate linear regression
   • (f) Write a short note on Support Vector Machines
9. Q4 Attempt the following (Any THREE) (Each of 5Marks) 15 marks
   • (a) Write a short note on Passive Reinforcement Learning
   • (b) Explain EM algorithm in detail
   • (c) Write a note on Naive Bayes models
   • (d) What are beta distributions? Elaborate with example
   • (e) Write a short note on Hidden Markov Model
   • (f) Write a note on Statistical Learning
10. Q5 Attempt the following (Any THREE) (Each of 5Marks) 15 marks
    • (a) Explain Uniform Cost Search with suitable example
    • (b) Write.a short note on Learning agent
    • (c) What is entropy? How do we calculate it?
    • (d) What is an artificial neuron network?
    • (e) Explain applications of Reinforcement Learning

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