Machine Learning (KME074) - 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 (KME074) — extracted question text · 100 marks · 3 Hours
Section AAttempt all questions in brief. 2 x 10 = 20
- aExplain the terms Artificial Int elligence (AI), Machine Learni ng (ML), and Deep Learning
- bWhat are the three main types of machine learning? Explain eac h type with an example
- cWhat is the implication of the use of a single learning rate ( η) for all features (xj) in gradient descent optimization?
- dDifferentiate between training data and testing data in machin e learning
- eDescribe dimension reduction in machine learning
- fDefine hierarchical clustering
- gExplain the term 'loss function' in deep learning
- hWhat is the meaning of Overfitt ing in Machine learning? 4 K1 i. Use a genetic algorithm to optimize a scheduling problem and e xplain each step. j. Define the term "kernel" in the context of Gaussian processes. 5 K 1
Section BAttempt any three of the following: 3 x 10 = 30
- aDescribe artificial intelligence (AI) and its potential impa ct on modern industries
- bWhat is the purpose of support vector machines (SVM), and ho w do they work?
- cWhat is the expectation-maximization algorithm? Provide a br ief overview
- dDiscuss the challenges and li mitations of the Backpropagatio n algorithm in neural network training
- eDefine Hidden Markov Models (HMM) and their applications
Section CAttempt any one part of the following: 1 x 10 = 10
- aWhat are the essential steps involved in designing a learnin g system? Explain each step briefly
- bExplain the applications of machine learning in mechanical engineering. Provide at least three use cases
- aExplain Bayesian Decision Theory
- bWhat are bias and variance in machine learning? How do they affect model performance?
- aDefine Principal Component Analysis (PCA) and its role in dimensionality reduction
- bWhat is clustering, and how does it differ from classification?
- aGiven stride S and kernel sizes for each layer of a (1-dimen sional) CNN, create a function to compute the receptive field of a part icular node in the network. This is just finding how many input nodes actually connect through to a neuron in a CNN
- bExplain the concept of a kerne l function in machine learning. How does it enable Support Vector Machines (SVM) to perform classificati on in higher-dimensional spaces? Provide examples of three commonly u sed kernel functions and their applications
- aCompare and contrast Bayesian estimation with Maximum Likeli hood Estimation (MLE). Highlight their advantages and disadvantages in the context of machine learning models
- bDiscuss the differences betwe en reinforcement learning and deep learning in the context of problem-solving
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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