Unsupervised feature selection models Permalink
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Live about unsupervised feature selection models for high-dimensional datasets
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KNMFS: a novel unsupervised feature selection method
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This was my guest lecture for the Roda de Conversa de Dados at CESAR. I introduced the audience to the concepts and challenges of feature engineering and presented KNMFS, a novel unsupervised feature selection method derived from my research.
Unsupervised Methods for High-dimensional data patterns
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This presentation was delivered to the ISG (Information System Group) group at CISUC. The main content addressed the exploration and development of unsupervised methods aimed at discovering patterns in high-dimensional data. The problem was contextualized by contrasting Small Data and Big Data, highlighting that smaller-scale data, despite quantitative challenges, offers greater explainability and focuses on “why” events happen.
HCI and the AI Revolution: Cognitive Engineering and Generative Design
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This lecture explored the core foundations of Human-Computer Interaction (HCI) and examined how Artificial Intelligence is transforming the field through Cognitive Engineering and Generative Design. It bridged traditional usability principles with modern AI-driven interface trends.