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

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