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Gujarat Technological University

This document is an examination paper for the Machine Learning subject at Gujarat Technological University, detailing the exam date, time, and total marks. It includes a series of questions covering various topics in machine learning such as linear regression, decision trees, reinforcement learning, and classification techniques. Students are instructed to attempt all questions and make suitable assumptions where necessary.

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20 views1 page

Gujarat Technological University

This document is an examination paper for the Machine Learning subject at Gujarat Technological University, detailing the exam date, time, and total marks. It includes a series of questions covering various topics in machine learning such as linear regression, decision trees, reinforcement learning, and classification techniques. Students are instructed to attempt all questions and make suitable assumptions where necessary.

Uploaded by

rozaseyoum26
Copyright
© © All Rights Reserved
We take content rights seriously. If you suspect this is your content, claim it here.
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Download as PDF, TXT or read online on Scribd
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Seat No.: ________ Enrolment No.

___________

GUJARAT TECHNOLOGICAL UNIVERSITY


ME - SEMESTER– 1 (NEW) • EXAMINATION – WINTER - 2021

Subject Code:3710216 Date:14 Mar 2022


Subject Name:Machine Learning
Time:10:30 AM TO 01:00 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 (a) What are the important objectives of Machine Learning? Discuss significant 07
examples of it.
(b) What do you mean by Gain and Entropy? How is it used to build the Decision 07
tree in algorithm? Illustrate using an example.
Q.2 (a) Explain in brief Linear Regression Technique. 07
(b) Explain Naïve Bayes classifier with an example. 07
OR
(b) Explain k-means clustering with example. 07
Q.3 (a) Describe a procedure of model selection and the estimate of the generalization 07
error, focusing on the case where a lot of data is available.
(b) Write a short note on Reinforcement Learning. 07
OR
Q.3 (a) Explain a Deep Learning in detail. 07
(b) Explain in detail Principal Component Analysis for Dimension Reduction. 07
Q.4 (a) Explain Brute force MAP hypothesis learner. What is Minimum Description 07
Length (MDL) principle?
(b) What are ensemble methods in Machine Learning? Explain Bagging along with 07
steps.
OR
Q.4 (a) Explain in brief a Probably Approximately Correct (PAC) Learning model in 07
Machine Learning.
(b) What is Support Vector Machine? How does it work? Detailing the advantages 07
and disadvantage of it.
Q.5 (a) Describe k-nearest neighbors algorithm. Why is it called instance based 07
Learning?
(b) Distinguish between Classification and Regression in Machine Learning. 07
OR
Q.5 (a) What is Supervised and Unsupervised Learning? Explain with the examples. 07
(b) Explain in brief: 07
1) Central Limit Theorem
2) Binomial Distribution

*************

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