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
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
Necessary prerequisites
- Algorithms
- Algebra
- Signal processing
Recommended prerequisites* :
- Programming in C
- Optimization
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- Algorithmic Development
- Linear Algebra
- Signal Processing
Recommended prerequisites:
- C Programming
- Optimization
Further information
CM: 18h
Practical work: 24h
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Taught lectures: 18 hours
Laboratory Practicals: 24 hours