<font size=5 color=blueyellow>**Development of algorithms for partial multi-label machine learning**</font> **Contents of this documents and quicklinks**： [TOC] ## Title ==**[ENCCS Webinar]: Development of algorithms for partial multi-label machine learning**== ## About the webinar Machine learning is a branch of artificial intelligence that enables computers to learn from data and make predictions or decisions without being explicitly programmed. Multi-label learning is a type of machine learning problem where each data instance can be associated with multiple labels simultaneously. Partial multi-label learning addresses problems where each instance is assigned a candidate label set and only a subset of these candidate labels is correct. Partial multi-label learning is particularly useful in scenarios where perfect labeling is expensive or impractical, making it an essential area in weakly supervised learning, however, a major of partial multi-label learning is that the training procedure can be easily misguided by noisy labels. In this webinar, we will talk about the general features of multiple partial multi-label methods, and then the development of learning algorithms to handle dataset with large noisy labels across different domains using varied frameworks, with a focus on the recently developed methods for partial multi-label learning based on the Encoder-Decoder framework. ## Who is the webinar for? This webinar is suitable for data scientists, software developers, scientific researchers, and AI practitioner who are: - being familiar basics of with machine learning - working on multi-label optimization and learning, and image processing - algorithm development for scientific packages ## Key takeaways After attending this seminar, you will: - Be familiar with classic optimization problems - Get to know an empirical application of Transformers - Understand the importance of algorithms design - Be aware of how speed enhancements are important to the results ## Speaker and moderataor [**Mengjie Han**](https://www.du.se/en/profile-page/?userId=1998840910) [Yonglei Wang](https://enccs.se/yonglei-wang) For any questions contact us at training@enccs.se ## Registration & Zoom link Register by visiting this link ==XXXXXXXXXXXXX== Zoom link: https://liu-se.zoom.us/j/61027533374?pwd=wji2q0z4sQcbIojpl8LkSNTIHLcHVX.1 Meeting ID: 610 2753 3374 Passcode: 267296 ## Disclaimer ~~Due to EuroCC2 regulations, we CAN NOT ACCEPT generic or private email addresses. Please use your official university or company email address for registration.~~ ~~This training is intended for users established in the European Union or a country associated with Horizon 2020. You can read more about the countries associated with Horizon2020 [HERE](https://ec.europa.eu/info/research-and-innovation/statistics/framework-programme-facts-and-figures/horizon-2020-country-profiles_e).~~ :::danger ::: :::danger :::