Enrolment No.
/Seat No_______________
GUJARAT TECHNOLOGICAL UNIVERSITY
BE- SEMESTER–VII (NEW) EXAMINATION – WINTER 2024
Subject Code:3171617 Date:19-11-2024
Subject Name: Applied 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.
4. Simple and non-programmable scientific calculators are allowed.
MARKS
Q.1 (a) How do Machine learn? 03
(b) Explain types of Machine Learning? 04
(c) What do you mean by a well-posed learning problem? Explain important features that 07
are required to well-define a learning problem Explain with suitable example.
Q.2 (a) Define : Training set, validation set and Testing set 03
(b) What are the main activities involved when you are preparing to start with modeling 04
in machine learning?
(c) Explain, in details, the process of evaluating the performance of a classification 07
model. Explain the different parameters of measurement.
OR
(c) Assume the confusion matrix of win/loss prediction of cricket match problem to be 07
as below and calculate Model accuracy, precision and recall.
Actual Win Actual Loss
Predicted Win 85 4
Predicted Loss 2 9
Q.3 (a) Differentiate between cross validation and bootstrapping. 03
(b) Define the terms: a) Nominal Data b) Ordinal Data 04
(c) Explain bias-variance trade-off in context of model fitting. 07
OR
Q.3 (a) State the merits and demerits of Bayes classifier. 03
(b) Define Covariance and Correlation. 04
(c) Can the performance of the learning model improved? If yes, explain how. 07
Q.4 (a) Define prior, posterior, and likelihood probability 03
(b) How to improve accuracy of the linear regression model? 04
(c) Explain SVM classification in detail. 07
OR
Q.4 (a) Define the terms MLE and MAP. 03
(b) Compare supervised learning and Unsupervised learning 04
(c) Explain Monte Carlo Approximation with suitable example. 07
Q.5 (a) Define RNN. 03
(b) Explain types of activation functions used in machine learning. 04
(c) What is Hypothesis? explain hypothesis testing. 07
OR
Q.5 (a) Define CNN. 03
(b) Explain Adversarial Attacks. 04
(c) Explain Generative Adversarial Networks in detail. 07
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