• Trity Course Scilab IoT

    Scilab for the Internet of Things

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  • Trity Course RPi IoT

    Raspberry Pi for the Internet of Things (with Pi-3)

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  • Trity Course Scilab AI

    Artificial Intelligence with Scilab

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  • Trity Course Scilab NCV

    Numerical Computation and Visualization with Scilab

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  • Trity Course Scilab IP

    Scilab for Image Processing and Computer Vision

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  • Trity Course Scilab BDA

    Big Data Training Series : Practical Guide to Big Data Analytics with Pig Latin, Hive and Scilab

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  • Trity Course Scilab DM

    Scilab for Data Mining

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  • Python Deep Learning

    Python for Machine and Deep Learning

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Scilab Courses

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Scilab is an open source, cross-platform numerical computational package and a high-level, numerically oriented programming language. It can be used for signal and image processing, statistical analysis, Internet of Things, data mining, etc. In Trity Technologies we have developed more than 20 courses based on Scilab since last few years.

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Raspberry Pi Courses

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The Raspberry Pi is a series of credit card–sized single-board computers developed in the United Kingdom by the Raspberry Pi Foundation with the intent to promote the teaching of basic computer science in schools and developing countries. Our very first Raspberry Pi Training is the aplication in IoT, and we are extending the training into other fields from time to time. 

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E4Coder - Automatic Code Generation

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E4Coder is a set of tools that can be used to simulate control algorithms and to generate code for embedded microcontrollers running with or without a realtime operating system. Our course focus on using the block diagram for algorithms development and the codes would be automatically generated and downloaded into the embedded boards such as Arduino Uno. A mobile robot application would be used for the training for practical hands-on. 

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Learning Support Vector Machines (SVM) Using SCILAB


Theory and Applications of Support Vector Machines (SVM) Using SCILAB

Support Vector Machines is an important topic in machine learning. The development of SVMs involved sound theory first, then implementation and experiments.

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Support Vector Machine is an important component in machine learning. With the integration with Scilab, one could focus on the learning the algorithms instead of programing.

Course Synopsis


This course serves as introduction of Support Vector Machines (SVM) and its applications using SCILAB. The course starts with the development of SVM and LibSVM. Then, we deliver the details and definition of Pattern Recognition Problems, Classification, Regression and Clustering.

Once the participants understand the concept of SVM and pattern recognition, as well as familiar with LibSVM, the course will be focusing on the engineering problems and respective solutions using LibSVM.


Course Objectives


This course is to introduce the popular state-of-art Support Vector Machines (SVM), deliver the concept of pattern recognition, i.e., classification, regression and clustering and to demonstrate the solutions of engineering problems using SVM.

Who Must Attend

Postgraduate students, Researchers, and Lecturers who need a powerful pattern recognition toolbox to complete their projects. Academician who wish to explore and integrate the newly computational intelligence techniques, i.e., Support Vector  Machines, into their respective field of research and anyone in Engineering who looks for alternative solutions for the real world engineering problems.


Basic Engineering Mathematics. Experience in SCILAB programming will be an advantage. Knowledge in Artificial Intelligence will be an advantage.

Course Outline

Introduction of Pattern Classification, Regression and Clustering

  • Theory and Development of Support Vector Machines (SVM)
  • LibSVM – A Powerful SCILAB module for SVM

Applications of Support Vector Classification (SVC)

  • Exercise of SVC on artificial data (with graphical results)
  • Power Systems – Conditional Monitoring of Transformer
  • Civil  Engineering – Occurrence of Flashover in Compartment Fire
  • Medical Diagnosis – Diagnostic of Breast Cancer

Applications of Support Vector Regression (SVR)

  • Exercise of SVR on artificial data (with graphical results)
  • Power Systems – Electrical Power Load Forecasting
  • Civil Engineering – Evacuation Times in Fire Event
  • Control Engineering – Identification and Control of Dynamic Systems

Introduction of Support Vector Clustering and exercise based on artificial data

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