B.TechSemester 52021-22Machine Learning TechniquesKCS-055

Machine Learning Techniques (KCS-055) - AKTU Question Paper 2021-22

B.Tech · Semester 5 · Free PDF Download

This is the official AKTU Machine Learning Techniques Previous Year Question Paper for B.Tech Semester 5, academic session 2021-22. Published by Dr. A.P.J. Abdul Kalam Technical University (AKTU/UPTU), Lucknow. Free PDF download — no login required.

Course:B.Tech

Semester:Semester 5

Session:2021-22

University:AKTU / UPTU

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Questions Asked in 2021-22

Machine Learning Techniques (KCS-055) — complete question paper

Section AAttempt all q u e s t i o n s i n b r i e f . 2 x 10 = 20

a
What is a “Well -posed Learni ng “problem? Explain with an example. 2 CO1
b
What is Occam's razor in ML? 2 CO1
c
What is the role of Inductive Bias in ANN? 2 CO2
d
What is gradient descent delta rule? 2 C O 2
e
What is Paired t Tests in H ypothesis evaluation? 2 CO3
f
How do you find the confidence interval for a hypothesis test? 2 C O 3
g
What is sample complexity of a Learning Problem? 2 CO4
h
Differentiate between Laz y and Eager Learning 2 CO4 i. What is the problem of crowding in GA 2 CO5 j. Comparison of purely analytical a nd purely inductive learning. 2 C O 5

Section BAttempt any three o f t h e f o l l o w i n g :

a
Design the Final design of ch eckers learning program. 10 CO1
b
What is Maximum Likelihood and Least Squared Error Hypothesi s? 10 CO2
c
What problem does the EM algorithm solve 10 CO3
d
Highlight the importance of Case Based Learning 10 CO4
e
Write short notes on Learni ng First Order Rules 10 CO5

Section CAttempt any one p a r t o f t h e f o l l o w i n g :

a
Explain the “Concept Learning” Task Giving an example 10 CO1
b
Find the maximally general hypothesis and maximally specific hypothesis for the training examples given in the table using the candidate elimination algorithm. Given Training Example: Sky Temp Humidity wind water Forecast sport Sunny warm Normal Strong warm same Yes Sunny warm High Strong warm same Yes Rainy cold High Strong warm change No Sunny warm High Strong cool change Yes
a
Comment on the Algorithmic convergence & Generalization property of ANN
b
Discuss the following issues in Decision Tree Learning
1
Overfitting the data
2
Guarding against bad attribute choices
3
Handling continuous valued attributes
4
Handling missing attribute values
5
Handling attributes with differing costs
a
How is Naïve Bayesian Classi fier different from Bayesian Classifier? 10 CO3
b
Explain the role of Central Lim it Theorem Approach for deriving Confidence Interval
a
Write short notes on Probably Approximately Correct (PAC) le arning model
b
Discuss various Mistake Bound Model of Learning 10 CO4
a
What is the significance of Learn -one Rule Algorithm? 10 CO5
b
Describe a prototypical gen etic algorithm along with variou s operations possible in it

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.