Global AI/ML Conference: Deep Learning & Reinforcement

Verified Event FCA2089052 - 2026 Conference

ICAIDLML 2026 - International Conference on Artificial Intelligence, Deep Learning and Machine Learning

The key intention of ICAIDLML is to provide opportunity for the global participants to share their ideas and experience in person with their peers expected to join from different parts on the world. In addition this gathering will help the delegates to establish research or business relations as well as to find international linkage for future collaborations in their career path. We hope that ICAIDLML outcome will lead to significant contributions to the knowledge base in these up-to-date scientific fields in scope.

Event Agenda

09:00 – 09:30 AM

Registration & Tea Coffee

09:30 – 10:00 AM

Inaugural Speech & Conference Theme Presentation

10:00 – 11:45 AM

Poster and Physical Presentation

11:45 – 12:30 PM

Virtual Presentation

12:30 – 01:30 PM

Lunch Break Break

01:30 – 02:15 PM

Award Function and Closing Ceremony Closing

• Deep learning
• Optimization
• Large-scale optimization
• Hyper-parameter optimization
• Model structure optimization
• Regularization
• Observation-dependent regularization
• Generative models as regularization: semi-supervised learning
• Structured learning
• Temporal models with long-term dependencies
• Deep learning with multiple modalities, including vision, speech and languages
• Unsupervised/generative modeling
• Efficient (Bayesian) inference for deep learning
• Large-scale generative modelling
• Reinforcement learning
• Learning representations for reinforcement learning
• Deep model-based and data-efficient reinforcement learning

• Artificial neural networks
• Association rule learning
• Automata, logic and games
• Bayesian networks
• Clustering
• Commercial software
• Commercial software with open-source editions
• Complex systems
• Computational complexity
• Computational learning theory
• Computational linguistics
• Computer animation
• Computer science
• Computer system
• Concurrent algorithms and data structures
• Data mining
• Decision tree learning
• Deep Learning
• Design and analysis of algorithms
• Genetic algorithms
• Inductive logic programming
• Intelligent systems
• Lambda calculus and types
• Logic and proof
• Machine learning
• Models of computation
• Object-oriented programming
• Open-source software
• Pattern recognition
• Reinforcement learning
• Representation learning
• Similarity and metric learning
• Sparse dictionary learning
• Supervised learning
• Support vector machines
• Unsupervised learning