ECTS
5 credits
Training Structure
College of Sciences
Description
Advanced Programming
- object-oriented programming (C++)
- classes
- attributes/methods
- legacy
- pointers
- templates
- C++11 standards
Artificial Intelligence
- Machine Learning: State of the Art, Challenges, 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
- Development of a Logic Simulator for Microelectronics
- Implementation (in C++) and integration (in ROS) of robotics algorithms
- Introduction to AI-Based Classification Tools
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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 of 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 (the concept of classes, inheritance, C++11 standards)
- Don't view C++ as an extension of C, but rather as a separate language that shares certain similarities
Artificial Intelligence
- become familiar with learning methods and their respective advantages, disadvantages, and 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 such as classes, inheritance, and C++11 standards)
- Learn to clearly distinguish between C++ and C programming
Artificial Intelligence
- understand various machine learning techniques, including their advantages, disadvantages, and target applications
- being able to choose the most appropriate machine learning technique for a given problem
Contact Hours:
Lectures taught: 18 hours
Laboratory Practicals: 24 hours
Mandatory Prerequisites
- Algorithms
- Algebra
- Signal Processing
Recommended prerequisites*:
- C Programming
- Optimization
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- Algorithm Development
- Linear Algebra
- Signal Processing
Recommended prerequisites:
- C Programming
- Optimization
Additional Information
CM: 6:00 p.m.
Practical Training: 24 hours
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Lectures taught: 18 hours
Laboratory Practicals: 24 hours