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.