SWARNANDHRA COLLEGE OF ENGINEERING & TECHNOLOGY
[AUTONOMOUS]
Seetharampuram, NARSAPUR-534 280
DEPARTMENT OF INFORMATION TECHNOLOGY
Mid – I
Part-A
Course/Sem: B.Tech-V Semester Branch: CSE-DS Date: 01-09-2025
Duration: 90 Min Subject: Machine learning Max. Marks: 30
------------------------------------------------------------------------------------------------------------------------------------------------------------
-
Answer ALL questions. SET-I
Course
Knowledge
Q.No Question Out Marks
Level
Come
Define Machine Learning. Explain its working process with a neat
1A K2 CO1 10M
diagram.
or
Describe the processes of Model Learning, Evaluation, and
2A K2 CO1 10M
Prediction in detail.
Explain the different types of data based on attribute/value in Machine
3A K1 CO2 10M
Learning. Give suitable examples for numerical and categorical data
or
Explain the working of KNN algorithm step by step with a small
4A K1 CO2 10M
numerical example.
Explain with examples the difference between metric and non-metric
5A K3 CO1 10 M
similarity functions in ML.
or
6A Explain the working of SVM algorithm step by step with An example. K2 CO2 10M
SWARNANDHRA COLLEGE OF ENGINEERING & TECHNOLOGY
[AUTONOMOUS]
Seetharampuram, NARSAPUR-534 280
DEPARTMENT OF INFORMATION TECHNOLOGY
Mid – I
Part-B
Course/Sem: B.Tech-V Semester Branch: CSE-DS Date: 01-09-2025
Duration: 20 Min Subject: Machine learning Max. Marks: 10
------------------------------------------------------------------------------------------------------------------------------------------------------------
-
Answer ALL questions. SET-I
Knowledge Course
Q.No Question Marks
Level Out Come
Give two examples of real-world Machine Learning
1 K2 CO1 2M
applications.
2 What is Model Prediction in ML? K2 CO1 2M
3 Write short on classification and regression K1 CO2 2M
Name two distance measures commonly used for numerical
4 K1 CO2 2M
attributes.
5 What is a proximity matrix? K2 CO2 2M
SWARNANDHRA COLLEGE OF ENGINEERING & TECHNOLOGY
[AUTONOMOUS]
Seetharampuram, NARSAPUR-534 280
DEPARTMENT OF INFORMATION TECHNOLOGY
Mid – I
Part-A
Course/Sem: B. Tech-V Semester Branch: CSE-DS Date: 01-09-2025
Duration: 90 Min Subject: Machine learning Max. Marks: 30
------------------------------------------------------------------------------------------------------------------------------------------------------------
-
Answer ALL questions. SET-II
Course
Knowledge
Q.No Question Out Marks
Level
Come
Explain the stages in the Machine Learning process with a neat
1 K2 CO1 10M
diagram.
or
Explain the importance of datasets in Machine Learning and discuss the
2 different types of datasets used. K2 CO1 10M
Discuss the importance of Data Acquisition and Feature
3 K2 CO2 10M
Engineering in ML projects.
or
Explain the K-Nearest Neighbor (KNN) algorithm in detail with a
4 K1 CO2 10M
working example. (k=5)
Explain the different types of attributes in Machine Learning and Data
5 Mining with examples. K1 CO1 10 M
or
Explain how distance measures like Euclidean and Manhattan distances
are used to compute similarity between numerical attributes in Machine
6 K2 CO2 10M
Learning. Give examples.
SWARNANDHRA COLLEGE OF ENGINEERING & TECHNOLOGY
[AUTONOMOUS]
Seetharampuram, NARSAPUR-534 280
DEPARTMENT OF INFORMATION TECHNOLOGY
Mid – I
Part-B
Course/Sem: B.Tech-V Semester Branch: CSE-DS Date: 01-09-2025
Duration: 20 Min Subject: Machine learning Max. Marks: 10
------------------------------------------------------------------------------------------------------------------------------------------------------------
-
Answer ALL questions. SET-II
Knowledge Course
Q.No Question Marks
Level Out Come
1 Write a short note model prediction K1 CO1 2M
2 List any two types of data used in Machine Learning. K2 CO1 2M
3 Name two non-metric similarity measures for binary attributes. K2 CO2 2M
4 What happens if the value of K is too small or too large in KNN? K1 CO2 2M
What is the purpose of normalization in distance-based algorithms?
5 K1 CO1 2M
SWARNANDHRA COLLEGE OF ENGINEERING & TECHNOLOGY
[AUTONOMOUS]
Seetharampuram, NARSAPUR-534 280
DEPARTMENT OF INFORMATION TECHNOLOGY
Mid – I
Part-A
Course/Sem: B.Tech-V Semester Branch: CSE-DS Date: 01-09-2025
Duration: 90 Min Subject: Machine learning Max. Marks: 30
------------------------------------------------------------------------------------------------------------------------------------------------------------
-
Answer ALL questions. SET-III
Course
Knowledge
Q.No Question Out Marks
Level
Come
Describe the different paradigms of Machine Learning with suitable
1 K1 CO1 10M
examples.
or
Describe the processes of Model Learning, Evaluation, and
2 K2 CO1 10M
Prediction in detail.
Explain the evolution of Machine Learning and discuss its major
3 K2 CO2 10M
milestones.
or
4 Explain the Naïve Bayes algorithm in detail with a working example K1 CO2 10M
Explain the Simple Matching Coefficient (SMC) and Jaccard Coefficient.
5 CO1 10 M
How are they used to measure similarity between binary attributes?
or K2
Discuss different classification algorithms based on distance measures
6 with examples. K2 CO2 10M
SWARNANDHRA COLLEGE OF ENGINEERING & TECHNOLOGY
[AUTONOMOUS]
Seetharampuram, NARSAPUR-534 280
DEPARTMENT OF INFORMATION TECHNOLOGY
Mid – I
Part-B
Course/Sem: B.Tech-V Semester Branch: CSE-DS Date: 01-09-2025
Duration: 20 Min Subject: Machine learning Max. Marks: 10
------------------------------------------------------------------------------------------------------------------------------------------------------------
-
Answer ALL questions. SET-III
Knowledge Course
Q.No Question Marks
Level Out Come
1 Define Machine Learning. K1 CO1 2M
2 Define proximity measure in Machine Learning. K1 CO2 2M
What is the difference between KNN Classification and KNN
3 K1 CO2 2M
Regression?
4 Differentiate between training and test datasets. K4 CO1 2M
Define a dataset in the context of Machine Learning.
5 K1 CO1 2M
