logo
Login

Pattern Recognition | IISC Bangalore: Comprehensive Introduction

IISC Bangalore

Comprehensive NPTEL course on pattern recognition techniques, featuring supervised and unsupervised learning, practical applications, and hands-on exercises.

University CoursesComputer VisionMachine Learning

Introduction

This NPTEL course provides a comprehensive introduction to pattern recognition, covering fundamental concepts, techniques, and applications. It explores various methods for feature extraction, classification, and clustering, equipping learners with the knowledge to tackle real-world pattern recognition challenges.

Highlights

  • Covers a wide range of pattern recognition techniques, including supervised and unsupervised learning
  • Emphasizes practical applications in areas such as image processing, speech recognition, and bioinformatics
  • Includes hands-on exercises and case studies to reinforce learning
  • Taught by experienced faculty from the Indian Institute of Science (IISc) Bangalore

Recommendation

This course is highly recommended for students, researchers, and professionals interested in developing a strong foundation in pattern recognition. It is particularly suitable for those pursuing careers in computer science, electrical engineering, data science, and related fields.

How GetVM Works

Learn by Doing from Your Browser Sidebar

Access from Browser Sidebar

Access from Browser Sidebar

Simply install the browser extension and click to launch GetVM directly from your sidebar.

Select Your Playground

Select Your Playground

Choose your OS, IDE, or app from our playground library and launch it instantly.

Learn and Practice Side-by-Side

Learn and Practice Side-by-Side

Practice within the VM while following tutorials or videos side-by-side. Save your work with Pro for easy continuity.

Explore Similar Hands-on Tutorials

Getting Started with Artificial Intelligence , 2nd Edition

25
Technical TutorialsData ScienceMachine Learning
Comprehensive introduction to AI, covering machine learning and data science. Practical guide to building enterprise applications with real-world examples.

Machine Learning For Dummies, IBM Limited Edition

19
Technical TutorialsData ScienceMachine Learning
Comprehensive guide to machine learning and data science, suitable for beginners and experienced professionals. Authored by experts Daniel Kirsch and Judith Hurwitz.

Data Mining Concepts and Techniques

25
Technical TutorialsData ScienceMachine Learning
Comprehensive coverage of data mining concepts and techniques, including data preprocessing, classification, clustering, and association rule mining. Essential resource for students, researchers, and professionals in data mining, machine learning, and data analysis.

A Brief Introduction to Machine Learning for Engineers

29
Technical TutorialsMachine Learning
Gain a solid understanding of machine learning concepts and techniques for engineers. Covers supervised, unsupervised, probabilistic models, and advanced topics.

A Comprehensive Guide to Machine Learning

24
Technical TutorialsData ScienceMachine Learning
Detailed resource on machine learning, data science, and artificial intelligence. Authored by experienced experts, suitable for beginners and experienced learners.

A First Encounter with Machine Learning

2
Technical TutorialsData ScienceMachine Learning
Explore fundamental machine learning concepts, algorithms, and applications in data science. Suitable for beginners interested in learning about this rapidly growing field.

A Selective Overview of Deep Learning

3
Technical TutorialsDeep LearningMachine LearningNeural Networks
Comprehensive overview of key concepts and recent advancements in deep learning, covering neural network models, training techniques, and theoretical foundations.

Algorithms for Reinforcement Learning

6
Technical TutorialsMachine LearningReinforcement Learning
Comprehensive guide to reinforcement learning algorithms, covering dynamic programming, temporal difference, Monte-Carlo methods, and more. Suitable for researchers, students, and practitioners in AI, ML, and control engineering.

Approaching Almost Any Machine Learning Problem

8
Technical TutorialsMachine LearningPython
Comprehensive guide to problem-solving approaches in machine learning, suitable for beginners and experienced practitioners. Covers a wide range of ML topics and techniques.

Discover categories

Computer GraphicsLinuxCDatabaseAlgorithmC++Artificial IntelligenceData StructuresGoDeep LearningComputer VisionRoboticsTensorFlowNetworkingGitData ScienceSoftware DevelopmentWeb DevelopmentMachine LearningCSSObject-Oriented ProgrammingProgrammingUnixPythonSQLBlockchainCompilerOperating SystemFunctional ProgrammingSecurityReactComputer ScienceNatural Language ProcessingJavaHTMLJavaScript