Teaching

Introduction to Artificial Intelligence for preparatory classes for higher education (CPES)

Introduction to Artificial Intelligence, Machine Learning, Deep Learning, training process, Neural Networks, evaluation, and associated tasks

Introduction to Artificial Intelligence for Applied Foreign Languages

Focus on Natural Language Processing, tasks, (past to modern) models, limits, and applications in professional settings

Introduction to Artificial Intelligence for Applied Foreign Languages

Introduction to Artificial Intelligence, Machine Learning, Deep Learning, training process, Neural Networks, evaluation, Natural Language Processing, and associated tasks

Introduction to Artificial Intelligence for Adult Education

Introduction to Artificial Intelligence, Machine Learning, Deep Learning, training process, Neural Networks, evaluation, Natural Language Processing, and associated tasks

Machine Learning & Semantic Web

Introduction to graph machine learning for knowledge graph refinement, some knowledge graph embedding models, link prediction, and evaluation

NoSQL Databases

Document-oriented databases (MongoDB), search engines (Elasticsearch), key-value databases (Redis), column-oriented databases (HBase), graph databases (Neo4J)

Introduction to Artificial Intelligence for preparatory classes for higher education (CPES)

Introduction to Artificial Intelligence, Machine Learning, Deep Learning, training process, Neural Networks, evaluation, and associated tasks

Introduction to Artificial Intelligence for Applied Foreign Languages

Introduction to Artificial Intelligence, Machine Learning, Deep Learning, training process, Neural Networks, evaluation, Natural Language Processing, and associated tasks

Introduction to Artificial Intelligence for Adult Education

Introduction to Artificial Intelligence, Machine Learning, Deep Learning, training process, Neural Networks, evaluation, Natural Language Processing, and associated tasks

Introduction to Artificial Intelligence for Marketing

Introduction to Artificial Intelligence, Machine Learning, Deep Learning, training process, Neural Networks, evaluation, Natural Language Processing, image generation, and associated tasks

Machine Learning & Semantic Web

Introduction to graph machine learning for knowledge graph refinement, some knowledge graph embedding models, link prediction, and evaluation

Research project supervision

Supervision of one master student for his research project

XML

XML language, DTD, XQuery, XSLT

Machine learning tutoring

Basics of Machine Learning, supervised and unsupervised learning, neural networks (MLP, CNN), training process, evaluation

IA - Artificial Intelligence

Artificial Intelligence, supervised and unsupervised learning (e.g., decision trees, neural networks, backpropagation algorithm, multilayer perceptron, K-means, Kohonen maps), Markov decision processes, knowledge graphs and the Semantic Web (RDF, ontologies, OWL reasoning, SPARQL), introduction to advanced AI techniques (e.g., CNN, GNN), applications & limits of AI, eXplainable AI, neuro-symbolic AI

SBC - Knowledge Based Systems

Semantic Web (RDF, ontologies, OWL reasoning, SPARQL)

Algorithmics

Introduction to algorithms (variables, assignments, conditions, loops, subalgorithms, etc.)

Web Technologies

HTML, CSS, and an introduction to JavaScript

Tools and Digital Culture

Proficient use of Word, Excel, and Internet Research

Programming with Java

Basic concepts of the Java language and Object Oriented Programming

Advanced Programming with Java

Advanced concepts of the Java language (collections, threads, User Interface, etc.)

Computational complexity

Best, worst, and average case complexity, sorting algorithms, recursion, de-recursion, randomized algorithms, greedy algorithms, dynamic programming, backtracking, data structures (stacks, queues, heaps, graphs)

Algorithms and Programming

Introduction to programming in Python

Semantic Web (in English)

First-order logic, Description Logics, Formal Concept Analysis, itemsets, association rules, Knowledge Graphs, Semantic Web (RDF, SPARQL) and applications

Computability and Complexity (in English)

Turing machines, uncomputability, undecidability, complexity (e.g., big O notation)

Introduction to GNU/Linux and Git (in English)

Introduction to GNU/Linux and Git for development and project management

Ontologies (in English)

Description Logics, SPARQL

IA - Artificial Intelligence

Search algorithms (e.g., A*, alpha-beta pruning), genetic algorithms, constraint satisfaction problems, Markov decision processes, supervised and unsupervised learning (e.g., decision trees, neural networks, backpropagation algorithm, multilayer perceptron, K-means, Kohonen maps)

SBC - Knowledge Based Systems

Semantic Web (RDF, ontologies, OWL reasoning, SPARQL), Prolog

NoSQL

Document-oriented databases (MongoDB), search engines (Elasticsearch), key-value databases (Redis), column-oriented databases (HBase), graph databases (Neo4J)

PCL - Compiler Implementation Project

IA - Artificial Intelligence

Search algorithms (e.g., A*, alpha-beta pruning), genetic algorithms, constraint satisfaction problems, Markov decision processes, supervised and unsupervised learning (e.g., decision trees, neural networks, backpropagation algorithm, multilayer perceptron, K-means, Kohonen maps)

SBC - Knowledge Based Systems

Semantic Web (RDF, ontologies, OWL reasoning, SPARQL), Prolog

PCL - Compiler Implementation Project

TOP - Computer Science Basics

Sorting algorithms, complexity, recursivity, back-tracking, Hoare logic

TRAD1 - Theory of Compilation

LR(0), SLR(1), LR(1) and LALR(1) parsers, Symbol Tables, Abstract Syntax Trees, assembly code generation

PCL - Compiler Implementation Project

PCL - Compiler Implementation Project