AI and software engineering: MLOps
Introduction to the practices of deploying, monitoring and maintaining machine learning models in production.
Teaching
I teach a variety of audiences, from undergraduate to master's level, within university programs. My courses mainly cover databases, data science and artificial intelligence, with an emphasis on hands-on approaches, modern data architectures and learning methods.
Advanced Topics in Machine Learning and Data Mining
Advanced course in machine learning and data mining, taught in English for a master's audience.
Data Mining
A complete data exploration workflow: univariate, bivariate and multivariate analysis, clustering methods and criteria for evaluating clusterings.
Deep neural networks
In-depth study of deep learning architectures and their training for vision and natural language processing applications.
Introduction to AI
Fundamentals of artificial intelligence, with use cases in machine learning and intelligent systems.
New database paradigms
Study of modern architectures for structured, semi-structured and relational data, with a focus on scalability.
Quality and beyond the relational model (NoSQL)
Exploration of document, key-value, column and graph models, as well as NoSQL use cases for unstructured and massive data.
Generative AI
Introduction to modern generative models, applications and ethical issues.