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) — extracted question text · 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. Check the original PDF for equations, diagrams and complete wording. Extracted: 2026-08-23.

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