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) — extracted question text
Section AAttempt all q u e s t i o n s i n b r i e f . 2 x 10 = 20
- aWhat is a “Well -posed Learni ng “problem? Explain with an example. 2 CO1
- bWhat is Occam's razor in ML? 2 CO1
- cWhat is the role of Inductive Bias in ANN? 2 CO2
- dWhat is gradient descent delta rule? 2 C O 2
- eWhat is Paired t Tests in H ypothesis evaluation? 2 CO3
- fHow do you find the confidence interval for a hypothesis test? 2 C O 3
- gWhat is sample complexity of a Learning Problem? 2 CO4
- hDifferentiate 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 :
- aDesign the Final design of ch eckers learning program. 10 CO1
- bWhat is Maximum Likelihood and Least Squared Error Hypothesi s? 10 CO2
- cWhat problem does the EM algorithm solve 10 CO3
- dHighlight the importance of Case Based Learning 10 CO4
- eWrite 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 :
- aExplain the “Concept Learning” Task Giving an example 10 CO1
- bFind 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
- aComment on the Algorithmic convergence & Generalization property of ANN
- bDiscuss the following issues in Decision Tree Learning
- 1Overfitting the data
- 2Guarding against bad attribute choices
- 3Handling continuous valued attributes
- 4Handling missing attribute values
- 5Handling attributes with differing costs
- aHow is Naïve Bayesian Classi fier different from Bayesian Classifier? 10 CO3
- bExplain the role of Central Lim it Theorem Approach for deriving Confidence Interval
- aWrite short notes on Probably Approximately Correct (PAC) le arning model
- bDiscuss various Mistake Bound Model of Learning 10 CO4
- aWhat is the significance of Learn -one Rule Algorithm? 10 CO5
- bDescribe 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. Check the original PDF for equations, diagrams and complete wording. Extracted: 2026-08-23.
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