B.TechSemester 72024-25Machine LearningKOE073

Machine Learning (KOE073) - AKTU Question Paper 2024-25

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 2024-25. 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:2024-25

University:AKTU / UPTU

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Questions Asked in 2024-25

Machine Learning (KOE073) — complete question paper · 100 marks · 3 Hours

Section AAttempt all questions in brief. 2 x 10 = 20

a
Describe the differences between well-defined and ill-defined learning problems
b
Describe the trade-off between bias and variance in machine learning
c
What is inductive bias in the context of decision tree learning?
d
Explain how overfitting can affect decision tree performance
e
What is hypothesis accuracy in machine learning?
f
What is Bayes theorem?
g
Explain the relationship between sample size and generalization error for finite hypothesis spaces
h
What is the mistake bound model of learning? 4 K1 i. What is a hypothesis space in machine learning? 5 K1 j. Explain the process of general-to-specific beam search in hypot hesis generation

Section BAttempt any three of the following: 10 x 3 = 20

a
Demonstrate an example of concept learning for a binary classif ication problem
b
Demonstrate how Adaline adjusts its weights during training
c
Describe the relationship between Bayes Optimal Classifier and posterior probabilities
d
Demonstrate the implementation of k-NN for a simple classification problem
e
Describe the advantages of FOIL over propositional rule learners

Section CAttempt any one part of the following: 10 x 1 = 10

a
Explain the working steps of the Find-S algorithm
b
Explain how the List-Then-Eliminate algorithm finds consistent hypotheses
a
Demonstrate the training of a perceptron using the perceptron learning rule
b
Demonstrate how to update weights using the Delta rule
a
Explain the steps of the EM algorithm: Expectation step and Max imization step
b
Describe how conditio nal probabilities are re presented in Bayes ian Belief Networks
a
Demonstrate the application of the mistake bound model to the P erceptron learning algorithm
b
Demonstrate the application of LWR to a simple regression dataset
a
Explain the key components of a reinforcement learning framewor k: states, actions, rewards
b
Describe the significance of the learning rate and discount factor in Q-learning

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.

Repeated Questions — KOE073

Questions that appeared in more than one session, found by comparing 3 years of Machine Learning papers (2022-23, 2023-24, 2024-25)

What is Bayes theorem?

Appeared in: 2022-23 · 2024-25

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