AI and Machine Lerning Model Paper [PDF]

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EURCS 606 B.Tech. Degree Examination CSE VI SEMESTER ARTIFICIAL INTELLIGENCE & MACHINE LEARNING (Effective from the admitted batch 2012-13) MODEL PAPER Time: 3 Hours Max. Marks : 60 -----------------------------------------------------------------------------------------------------------------------------Instructions: Each Unit carries 12 marks. Answer all units choosing one question from each unit. All parts of the unit must be answered in one place only. Figures in the right hand margin indicate marks allotted. -----------------------------------------------------------------------------------------------------------------------------UNIT-I 1. (a) Define Machine Learning and state any two applications of Machine Learning. 4 (b) State and explain the various phases of designing a checkers learning program. 8 OR 2. Write the Candidate-Elimination algorithm and trace it through the following 12

Smile Concept Learning Task. Example 1 2 3 4 5

Eyes Round Square Square Round Square

Nose Triangle Square Triangle Triangle Square

Head Round Square Round Round Round

FColor Purple Green Yellow Green Yellow

Hair Yes Yes Yes No Yes

Smile Yes No Yes No Yes

UNIT-II 3. (a) What are the characteristics of the problems suited for decision tree learning? (b) Consider the following set of training examples. Instance 1 2 3 4 5 6

Classification + + + -

A1 T T T F F F

4 8

A2 T T F F T T

(i) What is the Entropy of this collection of training examples with respect to

the target function classification? (ii) What is the information gain of A2 relative to these training examples? OR 4. (a) How do you avoid overfitting the data in decision tree learning? Discuss. 8 (b) Explain Bayes theorem with an example. 4 UNIT-III 5. (a) Explain any two limitations of Propositional logic. How can you overcome these 4 with the help of Predicate logic? (b) Write the algorithm to convert a well-formed formula into clause form. Trace it 8 through the example: ‘All Romans who know Marcus either hate Caesar or think that anyone who hates anyone is crazy’. OR 6. (a) Given the following information for a database: 8 A1: if x is on top of y, y supports x. A2: if x is above y and they are touching each other, x is on top of y. A3: a cup is above a book. A4: a cup is touching a book. (i) Translate statements A1 through A4 to clausal form. (ii) Show that ‘supports(book,cup)’ is true using Resolution. (b) Discuss forward and backward reasoning in brief, providing one example 4 for each. UNIT-IV 7. (a) For what kind of problems Neural Network is appropriate? 6 b.) Explain the functioning of Sigmoid threshold unit in Neural Network. 6 (or) 8. (a) How back Propagation Algorithm works in the learning process 8 (b.) What is the motivation in the design of Artificial Neural Networks? 4 UNIT-V 9. (a) What are the factors that motivated for Genetic Algorithms? 6 (b) How do Genetic Algorithms search to seek maximally fit hypothesis? 6

(or) 10. (a) Explain about the evolution of population overtime by using Genetic Algorithms. 6 (b) Briefly explain about the Genetic Programming with an example. 6

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