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.
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Questions Asked in 2024-25
Machine Learning Techniques (BCS055) — complete question paper · 70 marks · 3 Hours
- aWhat constitutes a well-defined learning problem in machine learning? Give an example
- bHow does machine learning differ from data science, and how do they complement each other?
- cExplain the role of the sigmoid function in logistic regressio n
- dWhat is the significance of the decision surface in Support Ve ctor Machines (SVM)?
- eWhat is overfitting in decision tree learning, and how can it be avoided?
- fExplain the role of the activ ation function in a perceptron. 4 K 2
- gDescribe the role of the rewa rd function in reinforcement learning
- aDescribe the steps involved in designing a machine learning system. Illustrate each step with a real-world example
- bExplore the difference betwee n Simple Linear Regression and Multiple Linear Regression. How does the inclusion of multiple features affect the model?
- cIllustrate the steps of the ID3 algorithm, including how it selects attributes and constructs the tree
- dDiscuss 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
- eWhat is the role of the Q-lea rning function in reinforcement learning? Explain how it helps the agent learn optimal actions and policies
- aDiscuss the history of machin e learning. Highlight major milestones and advancements in the field
- bCompare and contrast supervised, unsupervised, and reinforce ment learning. Provide examples where each type is applicable
- aConsider 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} - bFit 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
- aDescribe the concept of locally weighted regression (LWR). H ow does it differ from traditional regres sion models, and what are its practical applications?
- bGiven 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?
- aDerive the mathematical steps involved in backpropagation fo r training
- bExplain the architecture a nd functioning of Convolutional Ne ural Networks (CNNs)
- aExplain the concept of Reinforcement Learning (RL). Discuss how it differs from supervised and unsupervised learning
- bExplain 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. Hindi translations are omitted — every question is printed in English in the original paper. Last verified: 2026-08-23.
Repeated Questions — BCS055
Questions that appeared in more than one session, found by comparing 2 years of Machine Learning Techniques papers (2024-25, 2025-26)
Explain the architecture a nd functioning of Convolutional Ne ural Networks (CNNs)
Appeared in: 2024-25 · 2025-26
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