Scope of the Conference

The aim of the conference is to bring researchers working in the areas of machine learning and applications together. The conference will cover both theoretical and experimental research results. Submission of machine learning papers describing machine learning applications in fields like medicine, biology, industry, manufacturing, security, education, virtual environments, game playing and problem solving is strongly encouraged.

Topics of interest

  • Statistical Learning
  • Neural Network Learning
  • Learning Through Fuzzy Logic
  • Learning Through Evolution (evolutionary algorithms)
  • Reinforcement Learning
  • Multistrategy 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
  • Evolutionary Computation
  • Machine Learning and Natural Language Processing
  • Multi-Lingual Knowledge Acquisition and Representation
  • Grammatical Inference
  • Knowledge Discovery in Databases
  • Knowledge Intensive Learning
  • Machine Learning and Information Retrieval
  • Machine Learning for Bioinformatics and Computational Biology
  • 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
  • Hybrid Learning Algorithms
  • Deep Learning
  • Big data
  • Machine learning in:
    • Game playing and problem solving
    • Intelligent Virtual Environments
    • Industrial and Engineering Applications
    • Homeland Security Applications
    • Medicine, Bioinformatics and Systems Biology
    • Economics, Business and Forecasting Applications

Application of Machine Learning

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.