Explore the programs and courses offered by Artificial Intelligence
Browse Programs Admission InformationField: Mathematics and Computer Science
Sector: Computer Science
Specialization: Artificial Intelligence
This Master's degree aims to train professionals and researchers specialized in Artificial Intelligence, capable of designing, developing, and optimizing solutions based on advanced AI algorithms. It covers key areas such as deep learning, Edge AI, language models (LLM), multi-agent systems, and advanced optimization. The teaching approach emphasizes a dual skill set:
Expertise in AI modeling and development, enabling students to design and implement high-performance machine learning and deep learning models, as well as solutions based on Edge AI.
A specialization in intelligent and computational systems, including brain-inspired learning (STDP, LTP, LTD).
The core courses are primarily focused on the Master 1 program and include:
UEF1 (S1)
Subject 1: Mathematics for AI (MIA)
UEM1 (S1)
Subject 1: Data Mining (FD)
UEF3 (S2)
Subject 1: Databases for AI (BDIA)
Subject 2: Advanced Statistics for AI (SAIA)
The advanced topics covered in this Master's program include deep learning, machine learning, and certain areas of AI that could become major in the near future.
In the first semester of the Master's 1 program, students will be introduced to supervised and unsupervised machine learning, machine learning, data mining, and quantum computing.
UEF1
Subject 2: Machine Learning (ML)
UEF2
Subject 1 Multi-Agent Systems (MAS)
Subject 2 Unsupervised Learning and Clustering (ULC)
UEM1
Subject 2 Quantum Computing (QC)
During the second semester, students will continue their introduction to deep learning, exploring different metaheuristic algorithms and learning about promising spiking neural networks.
UEF4
Subject 1 Metaheuristics (MH)
Subject 2 Deep Learning (DL)
UEM2
Subject 1 Computational Neuroscience (CN)
Subject 2 Edge AI (EAI)
The final semester of this master's program will be dedicated to mastering advanced neural networks. (recurrent networks, attention, transformers) and the application of what was learned during the previous year:
UEF5
Subject 1 Deep Recurrent Networks (DRN)
Subject 2 Reinforcement Learning (RL)
UEF6
Subject 1 Natural Language Processing (NLP)
Subject 2 Computer Vision (CV)
UEM3
Subject 1 Uncertain Knowledge Models (UCM)
Subject 2 Image Indexing and Retrieval (IRI)
Access to Master 1 is open to candidates holding a license (LMD) in computer science or another recognized equivalent diploma.
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