B.TechSemester 52024-25Machine Learning TechniquesBCS055

Machine Learning Techniques (BCS055) - AKTU Question Paper 2024-25

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 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 5

Session:2024-25

University:AKTU / UPTU

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Questions Asked in 2024-25

Machine Learning Techniques (BCS055) — extracted question text · 70 marks · 3 Hours

Section AAttempt all questions in brief. 2 x 07 = 14

a
What constitutes a well-defined learning problem in machine learning? Give an example
b
How does machine learning differ from data science, and how do they complement each other?
c
Explain the role of the sigmoid function in logistic regressio n
d
What is the significance of the decision surface in Support Ve ctor Machines (SVM)?
e
What is overfitting in decision tree learning, and how can it be avoided?
f
Explain the role of the activ ation function in a perceptron. 4 K 2
g
Describe the role of the rewa rd function in reinforcement learning

Section BAttempt any three of the following: 07 x 3 = 07

a
Describe the steps involved in designing a machine learning system. Illustrate each step with a real-world example
b
Explore the difference betwee n Simple Linear Regression and Multiple Linear Regression. How does the inclusion of multiple features affect the model?
c
Illustrate the steps of the ID3 algorithm, including how it selects attributes and constructs the tree
d
Discuss the Self-Organizing Map (SOM) algorithm. How does SO M work for clustering and dimensionality reduction? Explain the differences between SOM and supervised learning models
e
What is the role of the Q-lea rning function in reinforcement learning? Explain how it helps the agent learn optimal actions and policies

Section CAttempt any one part of the following: 07 x 1 = 07

a
Discuss the history of machin e learning. Highlight major milestones and advancements in the field
b
Compare and contrast supervised, unsupervised, and reinforce ment learning. Provide examples where each type is applicable

Consider the following weather data to determine whether or not a

person will play tennis on a given day based on weather conditions:
Day Outlook Temperature Humidity Wind Play Tennis?
1 Sunny Hot High Weak No
2 Overcast Hot High Weak Yes
3 Rainy Cool Normal Weak No
4 Overcast Cool Normal Strong Yes
5 Rainy Mild Normal Weak Yes
6 Sunny Mild Normal Strong No
7 Overcast Mild High Strong Yes
8 Overcast Hot Normal Weak No
Apply Naïve Bayes Classifier to predict whether the person will play or
not play tennis on given conditi on {Outlook = Sunny, Temperatur e =
Cool, Humidity = High, Wind = Strong}

b
Fit a linear regression model for the dataset (𝑥, 𝑦ሻ: (1, 1.5), (2, 3.0), (3, 4.5), (4, 6.0) and predict 𝑦 for 𝑥 = 5
a
Describe the concept of locally weighted regression (LWR). H ow does it differ from traditional regres sion models, and what are its practical applications?
b
Given the following dataset wi th two attributes (A and B), a nd a target variable (T): T Yes No Yes No Yes No Calculate the entropy before the split and after the split on a ttribute A. Which attribute (A or B) gives the highest information gain?
a
Derive the mathematical steps involved in backpropagation fo r training
b
Explain the architecture a nd functioning of Convolutional Ne ural Networks (CNNs)
a
Explain the concept of Reinforcement Learning (RL). Discuss how it differs from supervised and unsupervised learning
b
Explain the components of a genetic algorithm. Discuss the r ole of chromosomes, genes, and the population in the GA cycle

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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