AMLDS 2025 aims to enhance the state-of-the-art in Machine Learning and Data Science, as well as other promising areas of computing, by encouraging fresh, high-quality research discoveries and inventive solutions to tough machine learning challenges. Researchers, academicians, and professionals from all over the world are invited to submit original, unpublished research papers from all perspectives, including theory, practice, experimentation, and review papers highlighting specific research domains for presentation in the conference's technical sessions.
TOPICS
▪Machine Learning Foundations
Machine Learning System Design
Machine Learning Optimization
Supervised Learning
Unsupervised Learning
Reinforcement Learning
▪Deep Learning and Data Engineering
Deep Neural Networks Optimization Algorithms
Deep Feedforward Networks
Regularization
Deep Convolutional Neural Networks
Deep Recurrent Neural Networks
▪Machine Learning and Data Engineering
Machine Learning in Data Lakes
Machine Learning based Data Integration and Data Interoperability
Machine Learning Data Pipelines
Machine Learning based Data Streaming
Machine Learning Relating to Knowledge and Data Management
▪Applications
Bioinformatics
Biomedical informatics
Computational Biology
Healthcare
Human Activity Recognition