BSc CS Sem 5 October 2025 October 2025 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 ANY FOUR from the following: (20M)
   • (a) Write States, Initial States, Actions, Transition Model, Goal Test and Path Cost for 8-puzzle problem
   • (b) do Mathematics and Psychology serve as foundational pillars of
   • (c) Write short note on Simple Reflex Agents
   • (d) Explain Iterative Deepening Depth First Search strategy with suitable
   • (e) What do you mean by PEAS? Mention it for Medical diagnosis system and
   • (f) the below search problem along with heuristic values: Start State Heuristic: h(n) Find the path using Greedy Best First Search Algorithm
4. Q2 Attempt ANY FOUR from the following: (20M)
   • (a) Explain the architecture of Fuzzy Logic System
   • (b) Explain the concept of Polynomial Regression
   • (c) Whatisa Backpropagation Neural Network and how does it work?
   • (d) What do you mean by Knowledge Representation? Explain any two types
   • (e) Explain the concept of Decision Tree. Build a decision tree for the
   • (f) What is overfitting and underfitting in machine learning? How do they
5. Q3 Attempt ANY FOUR from the following: (20M)
   • (a) Explain Bayesian Learning with an example
   • (b) Write a short note on Passive Reinforcement Learning
   • (c) Explain Q- Learning in detail
   • (d) Explain the concept of Unsupervised Learning
   • (e) Explain Maximum-likelihood parameter learning for Continuous models Consider the database given below
   • i) Support (Dosa)
6. Q4 Attempt ANY FIVE from the following: (15M)
   • (a) Define heuristic function. Give an example heuristic function
   • (b) Describe problem formulation of vacuum world problem
   • (c) Write note on
   • i) Universal Quantifier
   • (d) What is an Artificial Neural Network
   • (e) in short about hidden and Latent Variables
   • (f) a note on Naive Bayes models

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