Question 1
(a)
State Bayes Theorem and explain its significance.
3 Marks
(b)
Difference between joint probability and conditional probability
4 Marks
(c)
Explain of Autocorrelation, Cross-Correlation, and spectra in brief.
7 Marks
Gujarat Technological UniversitySummer 2026 Examination
GTU 3171613 Pattern Recognition Summer 2026 Paper Solution & PDF
B.E. · IT Engineering · Semester 7 · Subject Code: 3171613
Total Marks70 MarksExternal theory exam
Passing Marks23 Marks33% minimum cutoff
Exam Duration2.5 Hours2:30 PM – 5:00 PM
Paper Structure5 QuestionsWith internal OR choices
Question 2
(a)
Explain eigenvalues and eigenvectors in brief.
3 Marks
(b)
Explain the concept of a discriminant function in classification.
4 Marks
(c)
Explain the steps in “k-means clustering” using a suitable illustration. List out limitations of “k-means clustering”.
7 Marks
OR OPTION
(c)
Discuss the principles and applications of nonparametric techniques for density estimation, focusing on the K-Nearest Neighbors (K-NN) method. Provide examples of how K-NN can be employed in practice.
7 Marks
Question 3
(a)
Define Maximum-Likelihood Estimation (MLE) in the context of the Gaussian case.
3 Marks
(b)
Describe the key differences between Bayesian estimate and maximum likelihood estimation.
4 Marks
(c)
Explain Hidden Markov model (HMMs) and its role in the classifier design.
7 Marks
OR OPTION
(a)
Define the criterion function used in K-Means clustering.
3 Marks
(b)
Describe the key differences between Maximum a Posteriori (MAP) estimation and Maximum-Likelihood Estimation (MLE).
4 Marks
(c)
Explain the Expectation-Maximization (EM) algorithm in the context of Gaussian Mixture Models (GMMs) and demonstrate how it can be used to estimate the parameters of a GMM.
7 Marks
Question 4
(a)
Describe the main objective of Factor Analysis.
3 Marks
(b)
Explain Fisher Discriminant Analysis (FDA) in the context of supervised dimensionality reduction.
4 Marks
(c)
Explain in detail the “Principal Component Analysis” method for dimensionality reduction.
7 Marks
OR OPTION
(a)
Define Principal Component Analysis (PCA) and its purpose in dimensionality reduction.
3 Marks
(b)
Compare and contrast eigenvalues and singular vectors as dictionaries for data representation.
4 Marks
(c)
Analyze the role of dictionary learning methods, such as factor analysis and non-negative matrix factorization, in capturing latent features and reducing data dimensionality.
7 Marks
Question 5
(a)
Define linear discriminant functions and their role in classification.
3 Marks
(b)
State the main idea behind Support Vector Machines (SVM) for binary classification.
4 Marks
(c)
Explain Decision Tree learning based on the Classification and Regression Trees (CART) approach with example.
7 Marks
OR OPTION
(a)
Define nominal data in the context of pattern classification.
3 Marks
(b)
Define Gradient Descent, and how is it used in optimizing linear discriminant functions.
4 Marks
(c)
Discuss multilayer feed-forward neural networks with neat architecture diagram
7 Marks
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About this Examination Paper & Attribution
Official Gujarat Technological University (GTU) examination paper and step-by-step solutions for Pattern Recognition (Summer 2026, B.E. · IT Engineering, Sem 7). Features complete 70-mark regular & remedial examination pattern, official marking distribution across all 5 questions, and direct 1-click official PDF download.
Transcribed for student exam preparation from Gujarat Technological University official examination archives. Questions, syllabus guidelines, and curriculum marking schemes remain the intellectual property of Gujarat Technological University.