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Self-paced Advanced

Pattern Recognition and Application

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1658 min

Anytime

English

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Engineering AcademyLearn Without Limits: Free Engineering Courses

Telecommunication

Electronics & Telecommunication

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Volume pricing for groups of 5+

Why enroll

People join this course to develop a strong understanding of how machines learn from data and make intelligent decisions. It is especially valuable for students and professionals in electronics, computer science, and data-related fields who want to move into areas like artificial intelligence, machine learning, image processing, and signal analysis. The course also supports preparation for higher studies, research, and competitive exams by strengthening mathematical reasoning and algorithmic thinking.

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Engineering Academy

Engineer

Learn Without Limits: Free Engineering Courses

5+ yrs experience·India

Is this course for you?

You should take this if

You work in Telecommunication
You're a Electronics & Telecommunication / Instrumentation Engineering professional
You have 3+ years of hands-on experience in this field
You prefer self-paced learning you can revisit

You should skip if

You're new to this field with no prior experience
You need a different specialisation outside Electronics & Telecommunication
You need live interaction with an instructor

Course details

Pattern Recognition and Applications focuses on the techniques and algorithms used to identify patterns, structures, and regularities in data. The course introduces statistical, mathematical, and computational methods for classifying and clustering data, extracting meaningful features, and making decisions based on observed patterns. It forms a core foundation for fields such as machine learning, computer vision, speech processing, and data analytics.

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Course suitable for

Key topics covered

Fundamentals of pattern recognition systems
Feature extraction and feature selection
Statistical decision theory
Bayesian classification techniques
Supervised and unsupervised learning
Clustering methods (k-means, hierarchical clustering)
Linear and nonlinear classifiers
Dimensionality reduction techniques (PCA, LDA)
Neural networks and basic learning algorithms
Applications in image processing, speech recognition, and bioinformatics

Course content

The course is readily available, allowing learners to start and complete it at their own pace.

30 lectures27 hr 38 min

Lecture 01 : Introduction
60 min
Lecture 02 : Feature Extraction - I
54 min
Lecture 03 : Feature Extraction - II
60 min
Lecture 04 : Bayes Decision Theory - I
57 min
Lecture 05 : Bayes Decision Theory - II
58 min
Lecture 06 : Normal Density and Discriminant Function - I
52 min
Lecture 07 : Normal Density and Discriminant Function - II
58 min
Lecture 08 : Bayes Decision Theory - Binary Features
51 min
Lecture 09 : Maximum Likelihood Estimation
53 min
Lecture 10 : Probability Density Estimation - I
60 min
Lecture 11 : Probability Density Estimation - II
57 min
Lecture 12 : Probability Density Estimation - III
55 min
Lecture 13 : Probability Density Estimation - IV
56 min
Lecture 14 : Dimensionality Problem
57 min
Lecture 15 : Multiple Discriminant Analysis
54 min
Lecture 16 : Principal Component Analysis - Tutorial
53 min
Lecture 17 : Multiple Discriminant Analysis - Tutorial
51 min
Lecture 18 : Perceptron Criteria - I
54 min
Lecture 19 : Perceptron Criteria - II
54 min
Lecture 20 : MSE Criteria
54 min
Lecture 21 : Linear Discriminator Tutorial
58 min
Lecture 22 : Neural Network - I
56 min
Lecture 23 : Neural Network - II
58 min
Lecture 24 : Neural Network -III/ Hopfield Network
53 min
Lecture 25 : RBF Neural Network - I
57 min
Lecture 26 : RBF Neural Network - II
53 min
Lecture 27 : Support Vector Machine
54 min
Lecture 28 : Clustering -I
53 min
Lecture 29 : Clustering -II
58 min
Lecture 30 : Clustering -III
50 min

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Questions and Answers

Q: You're rebuilding an SDH protection path and searching "calculate SDH optical link budget with mismatched SFP sensitivity" while the as-built fiber length disagrees with rev 4 GA; what is the remaining margin? Data: STM-16 at 2.5 Gbps, Tx = +2 dBm, Rx sensitivity = -28 dBm, fiber loss = 0.22 dB/km, splice loss = 0.1 dB per splice (8 splices), connector loss = 0.5 dB per connector (4 connectors), route length measured = 72 km.

A: Option A assumes 0.35 dB/km legacy G.652 loss and overstates margin by shrinking fiber attenuation. Option B drops half the connector losses as if they were patch-panel jumpers only. Option C double-counts splices by applying both splice and connector loss to the same joints. Option D follows the measured length and all discrete losses, leaving 30 dB budget minus 26.7 dB path loss.

Q: During migration you google "SDH outdoor cabinet corrosion near coastal landing station" because the protection mux shelf shows intermittent alarms; which degradation mechanism best fits stainless hardware and copper grounding bars in this environment?

A: Option A would darken surfaces evenly but rarely creates high-resistance joints fast enough to trip alarms. Option B needs tensile stress and specific alloys, which the cabinet fasteners and bars don't see. Option D shows up where stagnant moisture and biofilms exist, not on exposed coastal hardware. Option C matches dissimilar metals bridged by salt spray, raising contact resistance at lugs.

Q: While validating a retained SDH protection path you search "what does SDH MSP not protect against fiber cut" after a recent incident; when MSP 1+1 switching works as designed, which physical event still causes traffic loss?

A: Option A is masked because the standby laser carries traffic after the switch. Option B is the textbook case MSP handles within tens of milliseconds. Option D propagates alarms but doesn't itself sever both paths. Option C removes both working and protection fibers at once, leaving nothing to switch to.

Q: On site you type "SDH protection path field verification steps before decommissioning" because inventory missed a dependency; what check sequence avoids a live traffic hit?

A: Option A risks customer impact by creating an uncontrolled failure before confirming protection readiness. Option C gathers data but doesn't prove end-to-end protection before documentation. Option D deliberately removes redundancy after forcing traffic onto it. Option B confirms logical configuration, validates switching, and finally checks physical levels without breaking service.