Undergraduate Program, Pontifícia Universidade Católica de Campinas, Engenharia de Computação, 2025
O curso Projeto Final I oferece a oportunidade de aplicar todos os conhecimentos adquiridos ao longo da graduação na concepção, desenvolvimento e validação de um projeto de engenharia de computação, com acompanhamento docente e foco em inovação, metodologia científica e responsabilidade ética.
Undergraduate Program, Pontifícia Universidade Católica de Campinas, Ciência de Dados e Inteligência Artificial, 2025
O curso de Sistemas Computacionais tem como objetivo desenvolver a capacidade de idealizar e prototipar sistemas computacionais em diferentes níveis de abstração.
Undergraduate Program, Pontifícia Universidade Católica de Campinas, Engenharia de Computação, 2025
O curso de Projetos de Sistemas Operacionais tem como objetivo estudar componentes e técnicas de gerenciamento de sistemas operacionais modernos, incluindo tópicos como processos, threads, escalonamento, entrada/saída e memória virtual.
Undergraduate Program, Pontifícia Universidade Católica de Campinas, Engenharia de Computação, 2025
O curso de Implementação de Núcleo de Sistema Operacional permite estudar e compreender as estruturas mais internas de um Sistema Operacional, construindo mentalmente um modelo de seu funcionamento.
Undergraduate Program, Pontifícia Universidade Católica de Campinas, Ciência de Dados e Inteligência Artificial, 2025
O curso de Fundamentos de Programação em Python tem como objetivo capacitar os alunos nos conceitos essenciais da programação, desde os princípios básicos até a introdução a estruturas de dados. Serão abordados conceitos fundamentais de informática, descrição de algoritmos por meio de pseudocódigo, fluxogramas e linguagem de programação, além do desenvolvimento da lógica de programação.
Undergraduate, University of Münster, Department of Information Systems, 2023
This course addresses how hardware and software systems are built, using abstraction, and how they work together. Students are guided to build a complete, general-purpose, and working computer system from ground up, starting with elementary logic gates. Official course website.
Master Program, University of Münster, Department of Information Systems, 2022
Database systems have been widely used in many real-world applications, due to their reliability and optimized operations. In this seminar, we research Machine Learning techniques that boost Data Management in highly demanding applications. We deep dive into the most recent developments from both research and industry. Official course website.
Undergraduate, University of Münster, Department of Information Systems, 2022
The Deep Learning with Python specialization module offers an in-depth exploration of neural networks, optimization algorithms, using popular Python libraries like TensorFlow and PyTorch. Through hands-on projects, participants gain practical experience in building and evaluating deep learning models for data science applications. Official course website.
Master Program, University of Münster, Department of Information Systems, 2022
This project seminar explores the application of deep learning techniques to develop an advanced search engine for satellite imagery. Participants will learn how to leverage deep neural networks like CNNs, UNets, ResNets to classify and analyze satellite images, enabling efficient and accurate retrieval of specific features and patterns from vast satellite image datasets. Official course website.
Master Program, University of Münster, Department of Information Systems, 2021
Data Integration is critical for diverse information system development tasks. In this course, a collection of tools and techniques is presented that can be applied in modern data integration tasks. Students are presented with the problems, solutions, and techniques related to data integration, and have the opportunity to apply the acquired knowledge techniques in practical scenarios. Official course website.
Master Program, University of Münster, Department of Information Systems, 2021
Machine Learning systems are extremely impacted by data quality and volume, where data access is generally a bottleneck (i.e., moving data around is costly). Since common ML systems architectures are inspired by DB systems, in this seminar we explore how data management techniques have provided effective solutions to make ML reliable and more efficient. Official course website.