# AI & DS Important Questions – IT sem 6 MU

AI & DS Important Questions – IT sem 6 MU

This material is contributed by Darshan and Mangesh. Make sure to follow them on their social handles:

**Mangesh Pangam**:- LinkedIn: Mangesh Pangam
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- Darshan
- Instagram : darshil2599

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### Module 1: Introduction to AI

**Weightage: 5 – 15 marks**

**Q1. What is PEAS? State and explain PEAS of automated taxi driver.****Q2. Illustrate with a diagram how a Goal Based agent works.****Q3. Illustrate the various types of Agents.****Q4. Draw and explain structure of rational agent.****Q15. Define an intelligent agent. Describe the structure of it & its components.**

### Module 2: Search Techniques

**Weightage: 15 – 25 marks**

**Q10. Write a note on Hill Climbing with a diagram. Explain the application of it.****Q11. Can 1 liter of water be measured using a 10-liter and 4-liter jug? Justify.****Q13. Compare different search techniques based on their time complexities. State the A* algorithm, explain with an example how it helps in finding the goal in the optimal path.****Q14. Using the Crypto-Arithmetic Problem as an example, describe how constraint satisfaction problems are formulated and solved. Provide a step-by-step solution to a simple Crypto-Arithmetic Problem.****Q25. Can min-max be used for team games? Draw trees for 2 & 3 teams.****Q28. Explain uniform cost search and best first search in detail with examples and compare. Also, compare min-max and alpha-beta pruning algorithms.**

### Module 3: Knowledge Representation using First Order Logic

**Weightage: 10 – 15 marks**

**Q12. Marcus was a man. Marcus was a Pompeian. All Pompeians were Romans. Caesar was a ruler. All Pompeians were either loyal to Caesar or hated him. Everyone is loyal to someone. People only try to assassinate rulers they are not loyal to. Marcus tried to assassinate Caesar. Was Marcus loyal to Caesar? Solve using resolution.****Q16. Differentiate Between Forward and Backward chaining.****Q17. What are the different planning techniques? Explain with example.****Q23. What are the rules of conversion from predicate to CNF? Explain each rule with a proper example.****Q24. Explain the WUMPUS WORLD Environment.****Q26. What is Unification? Give an example.****Q27. What is Skolemization? Explain Skolem constant & Skolem function.**

### Module 4: Introduction to DS

**Weightage: 5 – 10 marks**

**Q7. In detail, explain the steps in the Data Science Project.****Q19. Write a comparison between Business Intelligence and Data Science.**

### Module 5: Exploratory Data Analysis

**Weightage: 15 – 20 marks**

**Q5. What do you mean by EDA? Explain different categorizations of EDA. For each type of EDA explain 1 technique that belongs to it in detail.****Q6. Explain various measures of central tendency of statistical distribution.****Q18. What do you mean by covariance and correlation? Explain the range of coefficients of correlation & covariance. Calculate COV (Observed Value1, Observed Value2) and CORRCOV (Observed Value1, Observed Value2) for data.****Q21. Differentiate between Univariate, Bivariate, & Multivariate analysis.****Q30. What is ANOVA Technique? Explain different types of ANOVA.****Q31. Consider you are performing ML for predicting housing prices. You have trained three models and the following data summarizes the predicted house price by each model for 5 different trial runs. Perform One way ANOVA F Test on this data and comment on whether the mean house price predicted by models A, B, and C are the same with a level of significance of 0.05. (Use of F Table is allowed)**

### Module 6: Introduction to ML

**Weightage: 15 – 20 marks**

**Q8. Elaborate in detail the steps in developing a Machine Learning application with an architectural diagram.****Q9. What are the different types of Machine Learning algorithms? Give an example of each category. Write in detail issues in machine learning.****Q20. Explain Support Vector Machine (SVM) in detail.****Q22. Illustrate the difference between Classification and Regression.****Q29. Compare Linear Regression vs Logistic Regression with suitable diagrams and formulas.**