• ECTS

    5 credits

  • Component

    Faculty of Science

Description

Advanced programming

  • object-oriented programming (C++)
  • classes
  • attributes/methods
  • heritage
  • pointers
  • templates
  • C++11 standards

Artificial Intelligence

  • learning: state of the art, issues, applications
  • PCA (Principal Component Analysis)
  • SVM (Support Vector Machines)
  • generations 1 2 and 3 of neural networks (spike technologies, etc.)
  • neural network learning
  • convolutional neural networks
  • reinforcement learning
  • genetic algorithms

Practical work

  • Setting up a logic simulator for microelectronics
  • Implementation (in C++) then integration (in ROS) of robotics algorithms
  • Introduction to classification tools based on artificial intelligence
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  • Advanced Programming

    • object oriented programming (C++)
    • classes
    • attributes/methods
    • heritage
    • pointers
    • templates
    • C++11 standards

    Artificial Intelligence

    • Machine Learning: State of art, problems, applications
    • PCA (Principal Component Analysis)
    • SVM (Support Vector Machines)
    • Neural networks generations 1, 2 and 3 (spike technologies, etc)
    • Convolutional neural networks
    • Reinforcement learning
    • Genetic Algorithms

    Laboratory Practicals

    • Implementation of a logical simulator for microelectronics
    • Implementation (in C++) and integration (in ROS) of robotic algorithms
    • Introduction to classification tools based on artificial intelligence

     

 

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Objectives

Advanced programming

  • familiarize yourself with object-oriented programming (notion of class, inheritance, C++11 standards)
  • don't see C++ as a continuation of C, but rather as a separate language that shares certain similarities

Artificial Intelligence

  • become familiar with learning methods and their respective advantages/disadvantages/objectives
  • learn to choose the most appropriate method for solving a given problem
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  • Advanced Programming

    • learn object-oriented programming (notions of class, heritage, C++11 standards)
    • learn to clearly distinguish C++ from C programming

    Artificial Intelligence

    • understand various machine learning techniques, with their pros, cons and target applications
    • being capable of choosing the most appropriate machine learning technique for a given problem

     

    Contact Hours:

                Taught lectures: 18 hours

                Laboratory Practicals: 24 hours

     

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Necessary prerequisites

  • Algorithms
  • Algebra
  • Signal processing

 

Recommended prerequisites* :

  • Programming in C
  • Optimization
  • -----------------------------------------------------------------------------------------------------------------------------------------------------
    • Algorithmic Development
    • Linear Algebra
    • Signal Processing

     

    Recommended prerequisites:

    • C Programming
    • Optimization
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Further information

CM: 18h

Practical work: 24h

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Taught lectures: 18 hours

Laboratory Practicals: 24 hours

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