Focus and Scope:

  • Deep Learning Architectures and Applications
  • Reinforcement Learning and Autonomous Systems
  • Supervised and Unsupervised Learning Techniques
  • Probabilistic Graphical Models
  • Natural Language Processing and Understanding
  • Computer Vision and Image Recognition
  • Transfer Learning and Domain Adaptation
  • Bayesian Learning and Kernel Methods
  • Big Data Analytics and Machine Learning
  • Explainable AI and Interpretability in ML Models
  • Ethical Considerations in Machine Learning Applications
  • AI-driven Decision Making and Optimization
  • Applications of Machine Learning in Various Domains (Healthcare, Finance, IoT, etc.)
  • Novel Approaches and Innovations in Machine Learning Research

The Babylonian Journal of Machine Learning (BJML) aims to encompass 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.