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ML QP 1 CT-1

This document contains a machine learning exam for mechanical engineering students. It has 3 sections with multiple choice and numerical questions. Section A contains 10 short answer questions about machine learning concepts and applications. Section B has 2 long answer questions about supervised vs unsupervised learning and issues in machine learning. Section C asks students to explain machine learning system design and applications in websites like Netflix using examples. The questions test various course outcomes and cognitive skill levels based on Bloom's taxonomy.

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HARSH KUMAR GAUTAM
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71 views2 pages

ML QP 1 CT-1

This document contains a machine learning exam for mechanical engineering students. It has 3 sections with multiple choice and numerical questions. Section A contains 10 short answer questions about machine learning concepts and applications. Section B has 2 long answer questions about supervised vs unsupervised learning and issues in machine learning. Section C asks students to explain machine learning system design and applications in websites like Netflix using examples. The questions test various course outcomes and cognitive skill levels based on Bloom's taxonomy.

Uploaded by

HARSH KUMAR GAUTAM
Copyright
© © All Rights Reserved
We take content rights seriously. If you suspect this is your content, claim it here.
Available Formats
Download as PDF, TXT or read online on Scribd
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KIET Group of Institutions, Delhi - NCR, Ghaziabad

(Roll Number: )

(Department of Mechanical Engineering)


B. Tech. in Mechanical Engineering, 7th Semester
CT-1, (2021-22) Odd Semester
Course: Machine Learning (KME 074)
Duration: 2hrs Max. Marks: 60

Section-A
Attempt all the questions of this section (2X10=20)
Q. No. Question Marks CO BL/ KC
a Define the applications of machine learning? 2 1 1/C
b Define Machine learning? When to use it? 2 1 1/C
c List the differences between supervised and unsupervised learning 2 1 1/C
d State the example of classification problem. 2 1 1/C
e Explain the two applications of ML in manufacturing industry 2 1 2/C
1. f Explain artificial intelligence (AI) 2 1 2/F
g Discuss the meaning of data mining. 2 2 2/C
h How use the linear regression in ML? 2 2 3/P
i Relate the important objectives of machine learning? 2 2 3/P
j Differentiate between Training data and Testing Data 2 2 2/P
Section-B
Attempt all the questions of this Section (5X4=20)
Q. No. Question Marks CO BL/ KC
Differentiate between Supervised, Unsupervised and Reinforcement
2
Learning 5 1 2/C
OR
Differentiate between pattern recognition and image processing.
Explain the issues in Machine Learning
3 OR 5 1 2/P
Describe the reinforcement ML, with suitable example
Compare between Data science, deep learning and machine learning?
OR
4 5 1 4/F
Explain with a neat diagram, application of ML in healthcare and finical
sectors.
Show the types of regression analysis. Explain in brief with suitable
examples.
5 OR 5 2 3/M
Show the application of Supervised learning in retail shopping. How it
works?
Section-C
Attempt all the questions of this Section (10X2=20)
Q. No. Question Marks CO BL/ KC
Show the Steps for Designing a ML Learning System. Explain in the
term of chess game (example).
6 OR 10 1 3/M
how machine learning works with Netflix, Facebook and amazon
websites.
7 The following table shows the midterm and final exam grades obtained 10 2 6/P

• CO –Course Outcome, generally, refer to traits, knowledge, skill set that a


student attains after completing the course successfully.
• BL–As per Revised Bloom’s Taxonomy, Bloom’s Levels (BLs) are the
cognitive process levels viz. 1. Remember, 2. Understand, 3. Apply, 4. Analyze 5. Evaluate and 6. Create
• KC –As per Revised Bloom’s Taxonomy, Knowledge Categories (KCs) are F -
Factual, C - Conceptual, P – Procedural, M -Metacognitive
for students in a database course.
(i) Use the method of least squares to find an equation for the
prediction of a student’s final exam grade based on the
student’s midterm grade in the course.
(ii) Predict the final exam grade of a student who received an 86 on
the midterm exam.
X (Midterm exam) Y (Final exam)
72 84
50 63
81 77
74 78
94 90
86 75
59 49
83 79
65 77
33 52
88 44
81 90

OR
Is regression a supervised learning technique? Justify your answer.
Compare regression with classification with examples.

• CO –Course Outcome, generally, refer to traits, knowledge, skill set that a


student attains after completing the course successfully.
• BL–As per Revised Bloom’s Taxonomy, Bloom’s Levels (BLs) are the
cognitive process levels viz. 1. Remember, 2. Understand, 3. Apply, 4. Analyze 5. Evaluate and 6. Create
• KC –As per Revised Bloom’s Taxonomy, Knowledge Categories (KCs) are F -
Factual, C - Conceptual, P – Procedural, M -Metacognitive

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