International Conference on Applied Research in Machine Learning
Welcome to the webpage of the first international conference on applied research in machine learning (ICARML). This conference is an international forum that allow researchers, academics and members of industry in the domain of machine learning and artificial intelligence to share knowledge, research and experience. This is a joint conference between the University of Staffordshire and the Arab Academy for Science, Technology and Maritime Transport and will be jointly hosted by these institutions.
Scope
The International Conference on Applied Research in Machine Learning (ICARML) aims to bring together scientists, researchers and practitioners in the field of machine learning to share their experience and research. The conference covers both core concepts of machine learning, such as the development of novel machine learning architectures and algorithms, as well as novel applications of machine learning techniques to solve real-world problems in different domains. The conference will bring together leading academics, scientists and researchers in the domain of interest from around the world. The topics of interest for submission include, but are not limited to:
- Applications of Machine Learning in Robotics
- Applications of Machine Learning in Time Series Analysis
- Applications of Machine Learning in Computer Vision and Image Processing
- Applications of Machine Learning in Finance and Economics
- Applications of Machine Learning in Automatic Control Algorithms
- Applications of Machine Learning in Communications and Electronics Engineering
- Applications of Machine Learning in Computational Linguistics and Natural Language Processing
- Applications of Machine Learning in Architectural and Civil Engineering
- Applications of Machine Learning in Pharmaceutical Science
- Applications of Machine Learning in Data Science
- General Machine Learning (active learning, clustering, reinforcement learning, etc)
- Deep Learning (architectures, generative models, deep reinforcement learning, etc)
- Learning Theory (bandits, game theory, statistical learning theory, etc)
- Trust in Machine Learning (accountability, fairness, robustness)
We encourage papers that present novel solutions to socially relevant problems, and that address the issue of ethics, accountability and safety of AI systems. All papers will be reviewed in a double blind process and accepted papers must be presented at the conference.