Q.1
14 Marks(c)
Explain of Autocorrelation, Cross-Correlation, and spectra in brief.
Gujarat Technological UniversitySummer 2026 examination
GTU 3171613 Pattern Recognition Summer 2026 Question Paper & PDF
B.E. · IT Engineering · Semester 7 · Subject code 3171613
- Total
- 70 marks
- To pass
- 23 marks (33%)
- Duration
- 2.5 Hours 2:30 PM – 5:00 PM
- Questions
- 5 with internal choice
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14 of 25 questions on this paper were also asked in other years. Each is marked below with the years it appeared.See all 18 repeated 3171613 questions, most asked first
Q.2
14 Marks(b)
Explain the concept of a discriminant function in classification.
(c)
Explain the steps in “k-means clustering” using a suitable illustration. List out limitations of “k-means clustering”.
Also asked in Summer 2025 Q4b
OR
(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.
Also asked in Summer 2025 Q5b OR
Q.3
14 Marks(a)
Define Maximum-Likelihood Estimation (MLE) in the context of the Gaussian case.
(b)
Describe the key differences between Bayesian estimate and maximum likelihood estimation.
Also asked in Winter 2025 Q3a OR
(c)
Explain Hidden Markov model (HMMs) and its role in the classifier design.
Also asked in Winter 2025 Q2c
OR
(a)
Define the criterion function used in K-Means clustering.
(b)
Describe the key differences between Maximum a Posteriori (MAP) estimation and Maximum-Likelihood Estimation (MLE).
(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.
Also asked in Winter 2025 Q2c OR
Q.4
14 Marks(a)
Describe the main objective of Factor Analysis.
(b)
Explain Fisher Discriminant Analysis (FDA) in the context of supervised dimensionality reduction.
Also asked in Summer 2025 Q3c
(c)
Explain in detail the “Principal Component Analysis” method for dimensionality reduction.
OR
(a)
Define Principal Component Analysis (PCA) and its purpose in dimensionality reduction.
(b)
Compare and contrast eigenvalues and singular vectors as dictionaries for data representation.
(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.
Q.5
14 Marks(a)
Define linear discriminant functions and their role in classification.
(b)
State the main idea behind Support Vector Machines (SVM) for binary classification.
Also asked in Winter 2025 Q5c, Summer 2025 Q5a
(c)
Explain Decision Tree learning based on the Classification and Regression Trees (CART) approach with example.
Also asked in Winter 2025 Q5c OR, Summer 2025 Q5c OR
OR
(b)
Define Gradient Descent, and how is it used in optimizing linear discriminant functions.
Also asked in Winter 2025 Q4c OR
(c)
Discuss multilayer feed-forward neural networks with neat architecture diagram
Also asked in Summer 2025 Q4c
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About this paper
Official Gujarat Technological University (GTU) examination paper 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.