ECTS
5 credits
Training structure
Faculty of Science
Description
Advanced Programming
- object-oriented programming (C++)
- classes
- attributes/methods
- inheritance
- 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
- Implementation of a logic simulator for microelectronics
- Implementation (in C++) and 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 the 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
- become familiar with object-oriented programming (concepts of classes, inheritance, C++11 standards)
- Do not view 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 how to choose the most appropriate method for solving a given problem
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Advanced Programming
- learn object-oriented programming (concepts of class, inheritance, 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 able to choose the most appropriate machine learning technique for a given problem
Contact Hours:
Taught lectures: 18 hours
Laboratory Practicals: 24 hours
Mandatory prerequisites
- Algorithmic
- Algebra
- Signal processing
Recommended prerequisites:
- Programming in C
- Optimization
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- Algorithmic Development
- Linear Algebra
- Signal Processing
Recommended prerequisites:
- C Programming
- Optimization
Additional information
CM: 6 p.m.
Practical work: 24 hours
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Taught lectures: 18 hours
Laboratory Practicals: 24 hours