STEM

These approaches are particularly suitable for primary school students who need to acquire basic skills in the field of STEAM. Teachers also use the project as an opportunity for further professional development, for example by signing up for an online course from the beginning.

The results of the project will be shared through web platforms, leaflets, magazines, business meetings, open exhibitions and, of course, on the project website, which already has a huge collection of videos. See, for example, this video for the Belfast meeting:

• ASTRONOMY HOMEWORK
• PROGRAMMING HELP
• COMPUTER SCIENCE HOMEWORK
• ENGINEERING HOMEWORK

The project started in 2016 and will end in 2018. It includes seven countries - Turkey (project coordinator), Italy, the former Yugoslav Republic of Macedonia, Portugal, Romania, Spain and the United Kingdom

Feature Selection For Large Dimensional Contextual Data Using Discrete Projections
Nonnegative matrix factorization (NMF) is a major method in many computer vision and machine learning applications, which provides a powerful generalization error-minimization technique for many nonnegative matrix factorization (NMF) tasks. Nonnegative matrices are typically not well suited to general learning and data analysis because of their high dimensional structure. In this work, we present a nonnegative matrix factorization-based learning approach for both learning and modeling nonnegative matrix data with only nonnegative matrix feature vectors. We show that the proposed approach has the ability to learn for sparse nonnegative matrices, with the same data as sparse matrices, as well as the same datasets. As shown, this approach achieves good performance on very challenging NMF datasets, while achieving competitive error-minimization rates for both learning and modeling datasets.

Use links:

1. https://imageevent.com/dinwinchester/stemhelp
2. https://imageevent.com/dinwinchester/exellent
3. https://www.openstreetmap.org/user/alexwriter
4. https://www.openstreetmap.org/user/spacecube_40