B.Sc. (Computer Science) Sem 5 Artificial Intelligence Mar 2025 Question Paper - Mumbai University | munotes

Questions asked in this paper

• N.B. (1) All questions are compulsory. (2) Figures to the right indicate marks. (3) Illustrations, in-depth answers and diagrams will be appreciated. (4) Mixing of sub-questions is not allowed.

SECTION I - Q.1 (Attempt ANY FOUR from the following) 20 Marks

1. (a) Give brief explanation about foundation of AI.
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2. (b) Differentiate between informed and uninformed search.
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3. (c) Define Intelligent agent and explain structure of agent with diagram.
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4. (d) Elaborate 8-puzzle problem with its States, Initial State, Actions, Transition Model and Goal state.
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5. (e) Explain Depth First Search Algorithm with example.
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6. (f) Consider the following graph and given heuristic values. Use the greedy best first search technique and find the path and path cost. Let the starting node be A and the goal node be G. [Directed graph: A to B cost 5, A to D cost 3; B to C cost 1; C to E cost 6, C to G cost 8; D to E cost 2, D to F cost 2; E to B cost 4; F to G cost 3; G to E cost 4. Heuristic h(n): A 5, B 3, C 6, D 2, E 3, F 4, G 0.]
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SECTION II - Q.2 (Attempt ANY FOUR from the following) 20 Marks

1. (a) Explain the key concepts of propositional logic and demonstrate with examples.
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2. (b) Discuss the various stages of the Knowledge Cycle in AI.
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3. (c) What is bias and variance in the context of machine learning? Discuss the bias-variance trade-off.
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4. (d) Define an Artificial Neural Network and explain its key components.
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5. (e) Give a brief explanation of multi linear regression.
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6. (f) Describe how a Support Vector Machine works, along with an example.
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SECTION III - Q.3 (Attempt ANY FOUR from the following) 20 Marks

1. (a) What is association rule mining? Give applications of it.
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2. (b) Discuss the concept of reinforcement learning along with its different types.
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3. (c) Give an overview of Hidden Markov Model and discuss its steps.
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4. (d) Explain the Naive Bayes Classifier and discuss its underlying assumptions.
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5. (e) Provide a detailed explanation of Q-learning and how it works.
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6. (f) For the given transaction data set find the following: Support(Cheese), Support(Bread, Cheese), Confidence(Bread => Milk), Lift(Bread => Milk), Lift(Cheese => Juice). [100: Bread, Cheese; 200: Bread, Cheese, Juice; 300: Bread, Milk; 400: Cheese, Juice, Milk.]
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SECTION IV - Q.4 (Attempt ANY FIVE from the following) 15 Marks

1. (a) What does PEAS stand for? Explain it with an example.
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2. (b) Explain Discrete vs. Continuous task environment with suitable example.
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3. (c) Define entropy and information gain, and discuss their significance briefly.
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4. (d) List and discuss the types of reasoning.
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5. (e) How do supervised and unsupervised learning differ? Provide a comparison.
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6. (f) What is support, confidence and lift in association rule mining?
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Read from the scan above, so a character or two may differ. The scan is the original.