ADR Awareness Centre | Adra Association

The European AI, Data and Robotics stakeholders operate from diverse historic backgrounds. To tap into the potentials of the diverse knowledge and resources generated in the AI, Data, and Robotics ecosystem, an all-inclusive collaboration approach between these stakeholders is necessary to make the ADR resources available and accessible to all.

For this, the ADR Awareness Centre is developed as an open repository of ADR educational resources and materials to enable collaboration and alignment among all projects within the Partnership, relevant external projects, and the public. The Awareness Centre will collect, validate, and publish educational resources and materials related to AI, Data, and Robotics.

- Who is this for? Everyone. Every project and organization in Europe including the general member of the public
- What does this Centre provide? AI, Data, and Robotics educational resources and materials produced in the EU
- When a new resource becomes available? The list of the resources will be updated when a new resource is submitted and validated
- Why should I publish a resource? Firstly, publishing your materials will promote and make them accessible to all. Secondly, it increases the transparency of your work and improves your and your organizations reputation. Last but not the least, your material will help address ADR challenges and contribute to shaping the future of ADR in Europe.
- How can I contribute and publish my resources? The Centre designed a very simple process for you to submit and publish your resources. You only need to submit your resource here and we will review and get it published and publicly accessible to all.
- What kinds of resources can I publish? The Centre only collects and publishes ADR related educational resources that can be publicly available and accessible to all. Copyrighted and licensed contents will not be published in the Centre.

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Understanding and mitigating bias in AI automated systems

“The AI community has been focusing on developing fixes for harmful bias and discrimination, through so-called ‘debiasing algorithms’ that either try to fix data for known or expected biases, or constrain the outcomes of a given predictive model to p

Category

Systems, methodologies, hardware, and tools

Source

AI-OnDemand

Digital Image Filtering

This lecture overviews Image Filtering, which is very important to ensure high image quality and image denoising. It covers the following topics in detail: Image noise. 2D FIR filters, including Moving average filters.

Category

Systems, methodologies, hardware, and tools

Source

AI-OnDemand

Lecture notes on reinforcement learning

Reinforcement learning is an appealing subject. Firstly, it is a very general concept: an agent interacts with an environment with the goal to maximize the rewards it receives from the environment.

Category

Systems, methodologies, hardware, and tools

Source

AI-OnDemand

Stereotypes in Language & Computational Language Models

Combined knowledge from linguistics, psychology, and natural language processing.

Category

Systems, methodologies, hardware, and tools

Source

AI-OnDemand

Algebraic Graph Analysis

This lecture overviews Algebraic Graph Analysis that has many applications in Network Theory, Web Science and Social Media Analytics. It covers the following topics in detail: Graph Basics.

Category

Systems, methodologies, hardware, and tools

Source

AI-OnDemand

Introduction to Tropical Geometry and its Applications to Machine Learning

Tropical geometry is a relatively recent field in mathematics and computer science combining elements of algebraic geometry and polyhedral geometry.

Category

Systems, methodologies, hardware, and tools

Source

AI-OnDemand