AI Colloquium
The AI Colloquium is a series of lectures dedicated to cutting-edge research in the field of machine learning and artificial intelligence, coorganized by the Lamarr Institute for Machine Learning and Artificial Intelligence (Lamarr Institute), the Research Center Trustworthy Data Science and Security (RC Trust), and the Center for Data Science & Simulation at TU Dortmund University (DoDas).
Programme
Distinguished researchers deliver captivating lectures followed by vibrant discussions. However, unlike traditional colloquia, the AI Colloquium prioritizes interactive dialogue, fostering international collaboration. Conducted primarily in English, these 90-minute sessions feature hour-long lectures and 30-minute Q&A sessions. Join every Thursday at 10 AM c.t. for a stimulating exploration of cutting-edge topics. Whether in-person at our Lecture Room on Fraunhofer Strasse 25 or via Zoom, our hybrid format ensures accessibility for all.
| Day (usually) | Thursday |
| Start and end time | 10 AM c.t. - 12 AM |
| Duration of Presentation | 60 Minutes |
| Location (usually) | Lecture Room 303 3. Floor Fraunhofer Strasse 25 Dortmund |
Upcomming Events
AUTOML for Data Streams
- Resource-aware ML
- Trustworthy AI
Abstract: Learning from data streams is a hot topic in machine learning and data mining. In this talk, we present one of the first algorithms for online hyper-parameter tuning for streaming data. The Self Hyper-Parameter Tuning (SPT) algorithm is an optimisation algorithm for online hyper-parameter tuning from non-stationary data streams. SPT works as a wrapper over any streaming algorithm and can be utilised for classification, regression, and recommendation tasks.
Prof. Dr. João Gama
Bio - João Gama is a Full Professor at the School of Economics, University of Porto, Portugal. He received his Ph.D. in Computer Science from the University of Porto in 2000. He is EurAI Fellow, IEEE Fellow, and the Asia-Pacific AI Association Fellow. He is a member of the board of directors of the LIAAD, a group belonging to INESC Tec. His main contributions are learning from data streams, where he has an extensive list of publications. He is the Editor-in-Chief of the International Journal of Data Science and Analytics, published by Springer.
Past Events
AUTOML for Data Streams
- Resource-aware ML
- Trustworthy AI
Abstract: Learning from data streams is a hot topic in machine learning and data mining. In this talk, we present one of the first algorithms for online hyper-parameter tuning for streaming data. The Self Hyper-Parameter Tuning (SPT) algorithm is an optimisation algorithm for online hyper-parameter tuning from non-stationary data streams. SPT works as a wrapper over any streaming algorithm and can be utilised for classification, regression, and recommendation tasks.
Prof. Dr. João Gama
Bio - João Gama is a Full Professor at the School of Economics, University of Porto, Portugal. He received his Ph.D. in Computer Science from the University of Porto in 2000. He is EurAI Fellow, IEEE Fellow, and the Asia-Pacific AI Association Fellow. He is a member of the board of directors of the LIAAD, a group belonging to INESC Tec. His main contributions are learning from data streams, where he has an extensive list of publications. He is the Editor-in-Chief of the International Journal of Data Science and Analytics, published by Springer.
AUTOML for Data Streams
- Resource-aware ML
- Trustworthy AI
Abstract: Learning from data streams is a hot topic in machine learning and data mining. In this talk, we present one of the first algorithms for online hyper-parameter tuning for streaming data. The Self Hyper-Parameter Tuning (SPT) algorithm is an optimisation algorithm for online hyper-parameter tuning from non-stationary data streams. SPT works as a wrapper over any streaming algorithm and can be utilised for classification, regression, and recommendation tasks.
Prof. Dr. João Gama
Bio - João Gama is a Full Professor at the School of Economics, University of Porto, Portugal. He received his Ph.D. in Computer Science from the University of Porto in 2000. He is EurAI Fellow, IEEE Fellow, and the Asia-Pacific AI Association Fellow. He is a member of the board of directors of the LIAAD, a group belonging to INESC Tec. His main contributions are learning from data streams, where he has an extensive list of publications. He is the Editor-in-Chief of the International Journal of Data Science and Analytics, published by Springer.