B.TechSemester 72022-23Machine LearningKOE073

Machine Learning (KOE073) - AKTU Question Paper 2022-23

B.Tech · Semester 7 · Free PDF Download

This is the official AKTU Machine Learning Previous Year Question Paper for B.Tech Semester 7, academic session 2022-23. Published by Dr. A.P.J. Abdul Kalam Technical University (AKTU/UPTU), Lucknow. Free PDF download — no login required.

Course:B.Tech

Semester:Semester 7

Session:2022-23

University:AKTU / UPTU

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Questions Asked in 2022-23

Machine Learning (KOE073) — complete question paper

Section AAttempt all questions in brief. 2x10 = 20

a
Define Machine learni ng and its application
b
What do you mean by big data analysis
c
Define Binary Decision Tree
d
Discuss the weig ht and bias in ANN
e
How do you evaluate hypothesis?
f
What is Bayes Theorem?
g
What is finite and infinite hypothesis space?
h
What is PAC learning model in machine learning? (i) What is the meaning of Chr omosome in genetic algorithm? (j) What is the difference between reinforcement learning and A rtificial Intelligence?

Section BAttempt any three of the following: 10x3 = 30

a
What are the five limitations of machine learning? Explain with suitable example
b
Define the following in decision tree algorithm: (i) Entropy, (ii) Information gain, (iii) Gini index, (iv) Gain Ratio, (iv) Chi-Square
c
What is Naive Bayes classifier and how does it work? Explain th e advantages of Naive Bayes algorithm
d
What is the advantages & disadvantages of locally weighted regr ession? Explain with suitable example
e
Explain the main steps of genetic algorithm with suitable examples?

Section CAttempt any one part of the following: 10x1 = 10

a
Explain the difference between Find-S and candidate elimina tion algorithm?
b
What is the inductive bias in CNN? Explain the image identification with CNN
a
What is the difference between forward propagation and backward propagation in neural networks explain weight calculation for forward pass network?
b
Explain the steps of decision tree making, with following data set, also calculate the Calculate data set entropy and information gain. DAY Outlook Temperature Humidity Sun light Play cricket D1 Rainy Hot High Wea k No D2 Rainy Hot High strong No D3 Overcast Hot Hi gh Wea k Y e s D4 Sunny Mil d High Wea k Y e s D5 Sunny Cool Normal Wea k Y e s
a
Show the following data set through Naive Bayes Classifiers: (i) If the weather is sunny, then the Player should play or not? (ii) If the level of humidity is medium & spend of the wind is high and then then the plays should play or not? Case Outlook Temperature Humidity Windy Play Golf 0 Rainy Hot High FALSE No 1 Rainy Hot High TRUE No 2 Overcas t H o t H i gh FALSE Yes 3 Sunny Mild High FALSE Yes 4 Sunn y C o o l N o r m a l F A L S E Y e s 5 Sunny Cool Normal TRUE No 6 Overcas t C o o l N o r m a l T R U E Y e s 7 Rainy Mild High FALSE No 8 Rainy Cool Normal FALSE Yes 9 Sunny Mild Normal FALSE Yes 10 Rainy Mild Normal TRUE Yes 11 Overcas t Mild Hi gh TRUE Yes 12 Overcast Hot Normal FALSE Yes 13 Sunn y Mild Hi gh TRUE No Draw the cluster of following 8 points into 3 clusters: Use the k-means algorithm and Euclidean distance and take the I nitial cluster centers are A2(4, 6), A4(5, 8) & A8 (4,9). The solution up to two iterations
a
Show the application of Clust ering in various sectors, disc us with following examples: Marketing, Insurance, & Earth-quake studies
b
Show the application of supervised machine learning, explai n with suitable example
a
What are the 4 types of reinforcement? Explain any two
b
Is reinforcement learning Artific ial intelligence or Machine Le arning? also explain the data mining in ML

Question text is extracted from the official AKTU question paper PDF above. Hindi translations are omitted — every question is printed in English in the original paper. Last verified: 2026-08-23.