ICMLA 2026 aims to bring together researchers and practitioners to present their latest achievements and innovations in the area of machine learning (ML).

The conference provides a leading international forum for the dissemination of original research in ML, with emphasis on applications as well as novel algorithms and systems. Following the success of previous ICMLA conferences, the conference aims to attract researchers and application developers from a wide range of ML related areas, and the recent emergence of Big Data processing brings an urgent need for machine learning to address these new challenges. The conference will cover both machine learning theoretical research and its applications. Contributions describing machine learning techniques applied to real-world problems and interdisciplinary research involving machine learning, in fields like medicine, biology, industry, manufacturing, security, education, virtual environments,games, are especially encouraged.

Contributions describing applications of machine learning (ML)techniques to real-world problems, interdisciplinary research involving machine learning, experimental and/or theoretical studies yielding new insights into the design of ML systems, and papers describing development of new analytical frameworks that advance practical machine learning methods are especially encouraged.

The technical program will consist of, but is not limited to, the following topics of interest:

statistical learning
neural network learning
learning through fuzzy logic
learning through evolution (evolutionary algorithms)
reinforcement learning
multi-strategy learning
cooperative learning
planning and learning
multi-agent learning
online and incremental learning
scalability of learning algorithms
inductive learning
inductive logic programming
Bayesian networks
support vector machines
case-based reasoning
machine learning for bioinformatics and computational biology
multi-lingual knowledge acquisition and representation
grammatical inference
knowledge acquisition and learning
knowledge discovery in databases
knowledge intensive learning
knowledge representation and reasoning
machine learning and information retrieval
machine learning for web navigation and mining
learning through mobile data mining
text and multimedia mining through machine learning
distributed and parallel learning algorithms and applications
feature extraction and classification
theories and models for plausible reasoning
computational learning theory
cognitive modeling
deep and transfer learning
federated learning
machine learning on the edge
machine learning for computer vision
hybrid learning algorithms

Applications of machine learning in:

medicine, health, bioinformatics and systems biology
industrial and engineering applications
security applications
smart cities and autonomous driving
game playing and problem solving
intelligent virtual environments
economics, business and forecasting applications, etc.