Gujarat Technological UniversitySummer 2025 examination

GTU 3171114 Introduction of Machine learning Summer 2025 Question Paper & PDF

B.E. · EC Engineering · Semester 7 · Subject code 3171114
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Total
70 marks
To pass
23 marks (33%)
Duration
2.5 Hours 2:30 PM – 5:00 PM
Questions
5 with internal choice
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11 of 25 questions on this paper were also asked in other years. Each is marked below with the years it appeared.See all 14 repeated 3171114 questions, most asked first

Q.1

14 Marks
(a)
3 marks

Define machine learning. Compare machine learning and traditional programming solution in terms of data, program and solution.

(b)
4 marks
Explain concept of hypothesis. Explain various terminologies used in hypothesis.
(c)
7 marksAsked in 2 of 3 papers

Draw and explain terminologies related to decision tree. Enlist advantages and disadvantages of decision tree, as machine learning algorithm.

Also asked in Winter 2025 Q2c OR

Q.2

14 Marks
(a)
3 marksAsked in 3 of 3 papers
Explain concept of over fitting with suitable examples.

Also asked in Summer 2026 Q1b, Winter 2025 Q2a

(b)
4 marks
Draw and explain Schematic diagram of supervised learning.
(c)
7 marks

Take a suitable example and explain concept of “Linear Regression” with one variable.

(c)
7 marks

What is dimensionality reduction? With the help of suitable examples, explain types of dimensionality reduction.

Q.3

14 Marks
(a)
3 marks
Explain concept of Deep learning.
(b)
4 marksAsked in 2 of 3 papers
Enlist limitations of Bayes classification.

Also asked in Winter 2025 Q3a OR

(c)
7 marks
Explain logistic regression with necessary details.
(a)
3 marksAsked in 3 of 3 papers
Explain concept of Naïve Bayes classifier.

Also asked in Summer 2026 Q3b OR, Winter 2025 Q2b

(b)
4 marks

Explain in brief concepts of Prior and Posterior in Bayes theorem with examples.

(c)
7 marks

Explain concept of support vectors. Draw and explain SVM model with necessary details.

Q.4

14 Marks
(a)
3 marks
Enlist the basic architectures of neural network.
(b)
4 marks

Explain the role of activation function in neural network. Explain Sigmoid and linear activation function in details.

(c)
7 marks
Explain with necessary details basic perceptron learning algorithm.
(a)
3 marksAsked in 2 of 3 papers
Compare Soft Max and RELU as activation functions in deep neural network.

Also asked in Winter 2025 Q4b

(b)
4 marks
Enlist useful properties and capabilities offered by neural networks.
(c)
7 marksAsked in 3 of 3 papers

Explain in brief, back propagation learning algorithm with all the necessary steps.

Also asked in Summer 2026 Q4b, Winter 2025 Q4c

Q.5

14 Marks
(a)
3 marksAsked in 2 of 3 papers
List down types of Ensemble learning algorithms.

Also asked in Summer 2026 Q4a OR

(b)
4 marksAsked in 3 of 3 papers
Define and explain in brief VC dimension in context of machine learning .

Also asked in Summer 2026 Q4c OR, Winter 2025 Q5c

(c)
7 marksAsked in 2 of 3 papers
Explain Hierarchical clustering with necessary details.

Also asked in Summer 2026 Q5c

(a)
3 marksAsked in 2 of 3 papers
Explain bootstrap aggregation.

Also asked in Winter 2025 Q5a OR

(b)
4 marksAsked in 2 of 3 papers
Explain in brief concepts of ‘dimensionality reduction’ and ‘feature selection’.

Also asked in Winter 2025 Q3a

(c)
7 marks
Explain K-means clustering algorithm with necessary details.

More Introduction of Machine learning papers

Questions repeat across sessions. Working through two or three papers shows you which topics GTU asks most.

Summer 202670 marks · 5 questions
Winter 202570 marks · 5 questions

Other Semester 7 subjects

BE EC

3171104
Biomedical Electronics
3171105
Introduction of Artificial Intelligence
3171106
Wireless Sensor Networks
3171108
Internet of Things

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About this paper

Official Gujarat Technological University (GTU) examination paper for Introduction of Machine learning (Summer 2025, B.E. · EC Engineering, Sem 7). Features complete 70-mark regular & remedial examination pattern, official marking distribution across all 5 questions, and direct 1-click official PDF download.

Transcribed for student exam preparation from Gujarat Technological University official examination archives. Questions, syllabus guidelines, and curriculum marking schemes remain the intellectual property of Gujarat Technological University.

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