B.TechSemester 62023-24Machine Learning TechniquesKAI601
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Machine Learning Techniques (KAI601) - AKTU Question Paper 2023-24

B.Tech · Semester 6 · Free PDF Download

This is the official AKTU Machine Learning Techniques Previous Year Question Paper for B.Tech Semester 6, 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 6
Session:2023-24
University:AKTU / UPTU

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Questions Asked in 2023-24

Machine Learning Techniques (KAI601) — 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 .
  • a
    Name two types of learning commonly used in machine learning. 02
  • b
    Give an example of a supervised learning problem. 02
  • c
    What is logistic regression, and how does it differ from linea r regression? 02
  • d
    What are the three types of support vector kernels commonly us ed in SVMs? 02
  • e
    Define inductive bias in the context of decision tree learning . 02
  • f
    Describe the process of locally weighted regression in instanc e-based learning. 02
  • g
    Define perceptron and their role in artificial neural networks . 02
  • h
    What are the key characteristics of the Self-Organizing Map (S OM) algorithm? 02 i. Define Reinforcement Learning (RL) and explain its key components. 02 j. Discuss the components of a genetic algorithm. 02
Section BAttempt any three o f t h e f o l l o w i n g :
  • a
    Compare and contrast supervised, unsupervised, and reinforce ment learning approaches in machine learning
  • b
    Discuss the mathematical for mulation of linear regression, i ncluding the hypothesis function, cost function, and optimization algorithm used for parameter estimation
  • c
    Provide a detailed explanation of the ID3 algorithm used for constructing decision trees. Discuss the key steps involved in the iterative process of feat ure selection and node splitting
  • d
    Explain the fundamental concepts behind the Backpropagation Algorithm and its importance in training neural networks
  • e
    Explain the Q-learning algorithm and its role in Reinforceme nt Learning. 10
Section CAttempt any one p a r t o f t h e f o l l o w i n g :
  • a
    Discuss the role of Bayesian networks in representing probab ilistic relationships between variables. Explain how Bay esian networks are constructe d, updated, and utilized for inference in real-world scenarios
  • b
    Discuss the concept of model evaluation in machine learning. Compare and contrast evaluation metrics such as accuracy, precision, recall and F1-score
  • a
    Discuss the fundamental principles of the Expectation-Maximi zation (EM) algorithm and its role in probabilistic modeling and parameter estimation
  • b
    Define the concept of a hyperplane in the context of machine learning and Support Vector Machines (SVMs). Explain how hyperplanes are used as dec ision surfaces to separate classes in feature space
  • a
    Discuss how entropy measures the uncertainty or randomness o f a dataset and its role in quantifying the impurity of decision tree nodes
  • b
    Describe the Locally Weighted Regression (LWR) technique and its purpose in machine learning
  • a
    Examine the effect of pooling layer parameters, such as pool ing size and stride, on feature representation and network performance
  • b
    Describe the role of Convolutional Neural Networks (CNNs), i n diagnosing Diabetic Retinopathy
  • a
    Critically analyze the strengths and limitations of Genetic Programming compared to other machine learning techniques
  • b
    Critically analyze the trade-offs between exploration and ex ploitation in the GA cycle of reproduction

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.

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