B.TechSemester 72023-24Machine LearningKME074

Machine Learning (KME074) - AKTU Question Paper 2023-24

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 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 7

Session:2023-24

University:AKTU / UPTU

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Questions Asked in 2023-24

Machine Learning (KME074) — complete question paper · 100 marks · 3 Hours

Section AAttempt all q u e s t i o n s i n b r i e f . 2 * 1 0 = 2 0

a
Define Machine Learning and briefly explain its significanc e in today’s technological landscape
b
Differentiate between Artif icial Intelligence (AI) and Machine Learning (ML), highlighting their key distinctions
c
What is the main difference between classification and regression analysis in supervised learning?
d
Provide a brief overview of the types of support vector ker nels
e
Explain the Multidimensional Scaling
f
How does K-Means Clustering work in unsupervised learning?
g
Define the Back propagation Algorithm in neural networks
h
Outline the basics of the Decision Tree algorithm. (i) Explain the meaning of reproduction in genetic algorithm (j) Explain the difference between reinforcement learning and d eep learning

Section BAttempt any three o f t h e f o l l o w i n g : 1 0 * 3 = 3 0

a
Explain the fundamental concepts of machine learning and di scuss its significance in various fields. Provide examples of real-world applications of machine learning in mechanical engineering
b
Discuss the concept of bias and variance in the context of evaluating an estimator. How do these factors impact the performance of a machine learning model, and what strategies can be employed to strike a balance between them?
c
Explain the principles of unsupervised learning and delve i nto the workings of K-Means Clustering and the Expectation-Maximization Algorithm. Provide real-world examples where these techniques can be effectively applied
d
Explore the basics of Decision Trees, focusing on the ID3 A lgorithm and the role of information gain and entropy. Discuss the challenges associated with decision tree learning and potential solutions
e
Discuss the Genetic algorithm (GA) with suitable example. A lso explain its advantages and applications

Section CAttempt any one p a r t o f t h e f o l l o w i n g : 1 0 * 1 = 1 0

a
Discuss the key components involved in designing a machine learning system. Provide insights into the challenges and considerations during the design phase
b
Differentiate between data science and machine learning. Discuss the overlap and unique aspects of these two fields
a
Explore the principles of Support Vector Machines (SVM). Di scuss the types of support vector kernels and the challenges associated with SVM. Provide a case study on car price prediction using SVM
b
What is regression in machine learning? Explain with exampl es
a
Draw the cluster of following 8 points into 3 clusters: A1= (10,7), A2=(8,6), A3=(9,4), A4=(5,8), A5=(7,5), A6=(7,4), A7=(3,2), A8=(4,9). Use the k-means algorithm and Euclidean distance and take the Initial cluster centers are A2(8, 6), A4(5, 8) & A8 (4,9). The solution up to two iterations
b
Explore Multidimensional Scaling and Linear Discriminant An alysis in the context of unsupervised learning. How do these methods contribute to data analysis and pattern recognition?
a
Introduce neural networks, covering the perceptron and the Back propagation Algorithm. Explain the convergence analysis and the universal approximation theorem for the back propagation algorithm
b
Explore the concept of Convolutional Neural Networks (CNNs) and the different types of layers in CNN architecture. Provide a case study demonstrating the application of CNN in real-world scenarios such as self-driving cars or building a smart speaker
a
What is genetic algorithm? Explain with suitable example and give its advantages
b
Examine the principles of Reinforcement Learning and its ro le in training intelligent systems. Compare and contrast reinforcement learning with supervised and unsupervised learning. Provide examples of real-world scenarios where reinforcement le arning has been successfully applied

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 — KME074

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

Explain the Multidimensional Scaling