Wholeheartedly all the researches who work in all areas of Modern Machine Learning Technology to take part in the workshop and to publish the research results relevant to such main topics:

• Regression and Classification analysis
• Deep learning
• Autoencoders and Generative Models
• Gaussian Process Regression
• Bayesian Optimization
• Social Media Analytics
• Predictive Analytics
• Complex Systems & Network Data Analysis
• Text Mining and Data Mining
• Gradient Boosted Trees
• Explainable and Interpretable AI
• Support Vector Machines
• Hyperparameter Tuing and Auto ML
• Bayesian networks
• Unsupervised learning for clustering
• MCDM and Decision Support Systems
• Clustering and Unsupervised Learning
• Multi-objective and Non-Convex Optimization
• Group Decision Making
• Multiattribute Utility or Value Theory
• Behavioral Issues in Decision Making
• Human Preference Modelling
• Applications of MCDM and Optimization

Papers should be written in English, font Times 11pt. Regular papers should be at least of 11 pages, posters at least of 4 pages.  Authors are requested to follow the CEUR-WS template. You can download DOCX template here. The first page should contain the title of the paper, names and addresses of all authors (including e-mail and ORCID), an abstract (100-150 words) and a list of keywords.

Papers must be submitted electronically (in PDF format) using registration form in EasyChair.

Submissions should describe original research. Papers accepted for presentation at MoMLeT&DS Workshop cannot be presented or have been presented at another meeting with publicly available published proceedings. Papers that are being submitted to other conferences must indicate this on the title page, as it must be  stated on papers that contain significant overlap with previously published work.