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Seat No.: Enrolment
GUJARAT TECHNOLOGICAL UNIVERSITY
MCA Integrated VII- EXAMINATION SUMMER 2017
Subject Code: 4470601 Date:29/04/2017
Subject Name: Machine Learning
Time: 02.30 PM TO 05.00 PM Total Marks: 70
Instructions:
1. Attempt all questions.
2. Make suitable assumptions wherever necessary.
3. Figures to the right indicate full marks.
Q.1

What is Machine Learning? List advantages and limitations of the Machine Learning.
07

Explain step by step general processes one has to follow for completing a supervised machine learning project.
07
Q.2

Explain working of Candidate- Elimination algorithm. Compare it with FIND-S algorithm.
07

What is Information Gain Explain with example addressing calculation of Information Gain and its usage for selection of attributes.
07
OR

What is perceptron? What is Back Propagation Neural Network What is the differences between both of them?
07
Q.3

Derive the weight update rule for BPNN.
07

What is BAYESIAN belief network? Explain with example.
07
OR
Q.3

Draw the perceptron network with three inputs three output units. Write the formulas for calculating errors at each output unit and updating the weights.
07

What is Probably Approximately Correct framework(PAC)? Explain with details.
07
Q.4

Explain Nearest Neighbor Learning algorithm.
07

What is Case -Based Reasoning How it helps in learning? Explain with suitable example.
07
OR
Q.4

What is Eager Learning method? Explain any one Eager Learning method.
07

What is sequential learning algorithm? Explain with help of Learn-One-Rule paradigm.
07
Q.5

What is First-Order Horn Clauses? How it helps in learning? Explain with example.
07

Explain Explanation based learning with help of PROLOG-EBG algorithm.
07
OR
Q.5

Explain First Order Combined Learner (FOCL) algorithm with example.
07

What is reinforcement learning? What is the purpose of reward in this kind of learning? List 3 situations where this kind of learning is required.
07