B.TechSemester 52023-24Machine Learning TechniquesKCS055

Machine Learning Techniques (KCS055) - AKTU Question Paper 2023-24

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 2023-24. 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:2023-24

University:AKTU / UPTU

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Questions Asked in 2023-24

Machine Learning Techniques (KCS055) — extracted question text · 100 marks · 3 Hours

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

a
Discuss the important objectives of Machine Learning. 2 1
b
Discuss overfitting and underfitt ing situation in decision tree learning. 2 1
c
Discuss support vectors in SVM. 2 2
d
What is gradient descent delta rule? 2 2
e
Explain Case-based learning. 2 3
f
For which problem decision tr ee is best suitable. 2 3
g
Define the term ANN, and CNN. 2 4
h
Differentiate between Lazy and Eager Learning. 2 4 i. Comparison of purely analytical and purely inductive learning. 2 5 j. Define the term Offspring, Chromosome and Genes are used in GA. 2 5

Section BAttempt any three o f t h e f o l l o w i n g : 10 x 3 = 30

a
Compare Supervised and Unsupervised Learning Techniques with examples
b
Explain Maximum Likelihood and Least Squared Error Hypothesi s with example
c
Compare and contrast Informati on Gain, Gain Ratio, and Gini Index in detail
d
Explain the different layers used in convolutional neural ne twork with suitable examples
e
Discuss the applications of reinforcement learning. In whic h problems reinforcement learning is used?

Section CAttempt any one p a r t o f t h e f o l l o w i n g : 10 x 1 = 10

a
Compare regression, classification and clustering in machine l e a r n i n g along with suitable real life applications
b
Explain the “Concept Learning” Task Giving an example. 10 1
a
Explain hyperplane (decision boundary) in SVM. Categorize va rious popular kernels associated with SVM
b
Differentiate between Naïve Bayes classifier and Bayesian be lief networks. Give an application of Bayesian belief networks
a
Discuss Decision Tree and expl ain its working in detail. 10 3
b
Demonstrate K-Nearest Neighbor s algorithm for classification w ith th e help of an example
a
Illustrate backpropagation algor ithm by assuming the trainin g rules for output unit weights and Hidden Unit weights
b
Write short notes on Probably Approximately Correct (PAC) learn ing model
a
Explain Q-learning with its key terms, key feature and eleme nts. Discuss its applications used in real life
b
Define the term Genetic Algorithm. Discuss the working of Ge netic algorithm with the help of flowchart

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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