<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 [e-mail]

## 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).~~

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