Artificial intelligence (AI) techniques involve the use of methods and algorithms to develop intelligent systems capable of performing tasks that would otherwise require human intelligence, such as problem‐solving, decision‐making, and pattern recognition. AI has brought about significant impacts in various areas of society, including industry and the economy, healthcare, education, social impact, ethics, job displacement, and security. Its influence has transformed several industries such as healthcare, finance, transportation, and manufacturing, resulting in increased productivity, profitability, and the creation of new jobs. It has also facilitated the development of new treatments, personalized care for patients, and customized learning experiences for students. However, the ethical implications, job displacement, and security concerns associated with AI must be considered and addressed to ensure its responsible and sustainable use in society.
The objective of this training course is to provide an overview of the key concepts that underpin artificial intelligence. The course is designed to benefit doctoral students and individuals interested in AI applications by equipping them with the necessary knowledge to integrate and leverage AI techniques to solve problems.
This training course will cover some of the most significant topics, including:
MACHINE LEARNING
This technique involves training an algorithm to learn from data, allowing it to improve its performance on a task over time. Machine learning is often used for tasks such as image recognition, natural language processing, and predictive analytics
Pr.
Abdallah MERAOUMIA
Dr.
Abdeljalil GATAL
DEEP LEARNING
A subset of machine learning, deep learning uses neural networks with multiple layers to analyze and process data. This technique is often used for tasks such as speech recognition, image processing, and autonomous driving.
Pr.
Mohamed AMROUNE
Dr.
Mohamed Yacine HAOUAM
DATA SCIENCE
Data science involves the use of statistical and computational techniques to extract insights and knowledge from data. AI, on the other hand, involves the development of intelligent algorithms and systems that can perform tasks that typically require human intelligence, such as problem‐solving, decision‐making, and pattern recognition.
Dr.
Lakhdar LAIMECHE
Dr.
Issam BENDIB
PROTOTYPING WITH ARDUINO USING AI
A workshop on prototyping with Arduino, Mblock, and AI can teach beginners about the
intersection of electronics, programming, and artificial intelligence. Participants can learn
how to control electronic components and integrate AI technologies into their projects. The
workshop can include hands-on activities and collaborative projects. By the end of the
workshop, participants will have acquired the skills and knowledge to prototype their ideas
and experiment with new technologies
Dr.
Salima bourougaa-Tria.
Mr.
Kada Medjahed Hicham.
FUZZY SYSTEMS
Fuzzy systems are designed to handle uncertainty and imprecision in data and decision‐making. Fuzzy systems use fuzzy logic, which allows for reasoning with uncertain or vague information. Fuzzy logic is based on the concept of membership functions, which assign degrees of truth to variables based on how well they fit a given criteria. Fuzzy systems have been applied in a wide range of fields, such as control systems, expert systems, and decision support systems.
Dr.
Djamel Ouannas
Dr.
Abdelazziz AOUICHE
METAHEURISTICS
Metaheuristics refer to a class of optimization algorithms that are designed to solve complex problems for which traditional optimization methods are not effective.
Metaheuristics are heuristic in nature, meaning they are designed to explore the search space of a problem solution by making use of probabilistic and adaptive methods. Examples of popular
metaheuristics include genetic algorithms, simulated annealing, tabu search, and particle swarm optimization.
Dr.
Latra YOUSFI
Dr.
Abdelhamid Djari
GREEN HYDROGEN
a solution for large-scale renewable energy storage
Moderator: Dr. Rabah Kadi, Researcher, Sonatrach
One of the major limitations of renewable energy sources such as solar and wind power is their intermittency, meaning their production is random and fluctuates depending on weather conditions. Green hydrogen can be used to store this excess renewable energy produced during periods of surplus production, to be used when renewable energy production is insufficient. This makes it an interesting solution for storing large amounts of renewable energy on a large scale, allowing for more flexible use and more efficient management of power grids. Additionally, the use of green hydrogen as a source of energy can help reduce greenhouse gas emissions and achieve CO2 reduction targets.
