Bernhard Pfahringer
24/04/2024
Your data is not i.i.d.: Learning from Data Streams
Recording
University of Waikato, New Zealand
Bernhard Pfahringer received his PhD degree from the University of Technology in Vienna, Austria, in 1995. He is a Professor with the Department of Computer Science, and a co-director for the AI Institute at the University of Waikato in New Zealand. His interests span a range of data mining and machine learning sub-fields, with a focus on streaming, randomization, complex data, and, inevitably over the last few years, Deep Learning as well.
I will first give a quick summary of Learning from Data Streams, and of Continual Learning, including some recent work on Online Continual Learning. I will give an overview of the TAIAO project, which stands for "Time-Evolving Data Science and Artificial Intelligence for Advanced Open Environmental Science". Finally, I will quickly present the works of my current and recently finished PhD students, comprising the following topics: - Advanced Adaptive Classifier Methods for Data Streams - SO-KNL: Self-optimising K-Nearest Leaves Regression Ensembles - Anomaly Detection in Streaming Data - AutoML for Data Streams - Self-supervised Feature Extractor Training for Alzheimer's Disease Classification - Feature Extractor Stacking for Cross-domain Few-shot Learning - ML Approaches for Malware Classification based on Hybrid Artefacts - Using LLMs to assess cybersecurity thread notes - Fake News detection in Urdu - Normalising Flows for Environmental Data - Fast Clustering using GPUs, 13:00.
24/04/2024
Your data is not i.i.d.: Learning from Data Streams
Recording
I will first give a quick summary of Learning from Data Streams, and of Continual Learning, including some recent work on Online Continual Learning. I will give an overview of the TAIAO project, which stands for "Time-Evolving Data Science and Artificial Intelligence for Advanced Open Environmental Science". Finally, I will quickly present the works of my current and recently finished PhD students, comprising the following topics: - Advanced Adaptive Classifier Methods for Data Streams - SO-KNL: Self-optimising K-Nearest Leaves Regression Ensembles - Anomaly Detection in Streaming Data - AutoML for Data Streams - Self-supervised Feature Extractor Training for Alzheimer's Disease Classification - Feature Extractor Stacking for Cross-domain Few-shot Learning - ML Approaches for Malware Classification based on Hybrid Artefacts - Using LLMs to assess cybersecurity thread notes - Fake News detection in Urdu - Normalising Flows for Environmental Data - Fast Clustering using GPUs, 13:00.
Bernhard Pfahringer
University of Waikato, New Zealand
Bernhard Pfahringer received his PhD degree from the University of Technology in Vienna, Austria, in 1995. He is a Professor with the Department of Computer Science, and a co-director for the AI Institute at the University of Waikato in New Zealand. His interests span a range of data mining and machine learning sub-fields, with a focus on streaming, randomization, complex data, and, inevitably over the last few years, Deep Learning as well.