Focus & Scope

 

The Babylonian Journal of Machine Learning (BJML) encompasses a wide array of topics within the realm of machine learning, encouraging scholarly contributions and discussions that contribute to the advancement of this field's theoretical foundations and practical applications.

Deep Learning

  • Architectures and Applications
  • Transfer Learning and Domain Adaptation
  • Explainable AI and Interpretability

Learning Techniques

  • Supervised and Unsupervised Learning
  • Reinforcement Learning
  • Bayesian Learning and Kernel Methods

Applications

  • Natural Language Processing
  • Computer Vision
  • Big Data Analytics
  • AI-driven Decision Making

Ethical Considerations

  • Ethics in ML Applications
  • Innovations in ML Research
  • Optimization and Decision Making