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.