B.TechSemester 72024-25Artificial IntelligenceKDS071
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Artificial Intelligence (KDS071) - AKTU Question Paper 2024-25

B.Tech · Semester 7 · Free PDF Download

This is the official AKTU Artificial Intelligence 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

Artificial Intelligence (KDS071) — complete question paper · 100 marks · 3 Hours

Section AAttempt all questions in brief. 2 x 10 = 20
  • a
    List examples of convex opt imization problems
  • b
    State the Karush-Kuhn-Tuck er (KKT) conditions
  • c
    State the basic steps of the mirror descent algorithm
  • d
    Define proximal gradient methods
  • e
    What are monotone operators?
  • f
    Illustrate the intuition behind Douglas–Rachford splitting
  • g
    What is Langevin dynamics?
  • h
    State the purpose of averaging methods in stochastic optimization. 4 K2 i. Differentiate supervised and unsupervised learning. 5 K1 j. What do you mean by feature ex traction in machine learning
Section BAttempt any three of the following: 10 x 3 = 20
  • a
    Illustrate how duality is appl ied in optimization problems with a simple example
  • b
    Explain the connection between ODE interpretations and optim ization algorithms
  • c
    Use the Augmented Lagrangian method to solve a constrained optimization problem with quadratic constraints
  • d
    How does Polyak–Juditsky aver aging smooth the optimization trajectory?
  • e
    Discuss various application domains of machine learning. 5 K 2
Section CAttempt any one part of the following: 10 x 1 = 10
  • a
    Apply the KKT conditions to solve a simple quadratic program ming problem
  • b
    Derive the dual problem for a given primal linear programmin g problem
  • a
    Apply the Frank–Wolfe method to solve a simple linear programming problem
  • b
    Use gradient descent to min imize the quadratic function f(x)=x2+4x+4
  • a
    Compare and contrast the Augmented Lagrangian method with th e standard Lagrangian approach
  • b
    Analyze the convergence prope rties of ADMM for non-smooth problems
  • a
    Apply Polyak–Juditsky averagi ng to improve the convergence o f an iterative algorithm
  • b
    Implement SVRG to optimize a logistic regression problem
  • a
    Explain use case on Bayesian classifier
  • b
    Create a real-world optimization scenario to demonstrate the use of PCA

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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