Cybernetics is concerned with describing complex interactions and interrelationships between systems which are omnipresent in our daily life. Machine Learning discovers fundamental functional relationships between variables and ensembles of variables in systems. The merging of the disciplines of Machine Learning and Cybernetics is aimed at the discovery of various forms of interaction between systems through diverse mechanisms of learning from data.The International Journal of Machine Learning and Cybernetics (IJMLC) focuses on the key research problems emerging at the junction of machine learning and cybernetics and serves as a broad forum for rapid dissemination of the latest advancements in the area. The emphasis of IJMLC is on the hybrid development of machine learning and cybernetics schemes inspired by different contributing disciplines such as engineering, mathematics, cognitive sciences, and applications. New ideas, design alternatives, implementations and case studies pertaining to all the aspects of machine learning and cybernetics fall within the scope of the IJMLC.Key research areas to be covered by the journal include:Machine Learning for modeling interactions between systemsPattern Recognition technology to support discovery of system-environment interactionControl of system-environment interactionsBiochemical interaction in biological and biologically-inspired systemsLearning for improvement of communication schemes between systems

控制論涉及到描述我們日常生活中無所不在的系統之間復雜的相互作用和相互關系。機器學習發現系統中變量和變量集合之間的基本函數關系。機器學習和控制論學科的融合旨在通過不同的數據學習機制發現系統之間的各種形式的交互作用。國際機器學習與控制論雜志（IJMLC）關注機器學習與控制論結合處出現的關鍵研究問題，并作為快速傳播該領域最新進展的廣泛論壇。IJMLC的重點是機器學習和控制論方案的混合開發，這些方案受工程、數學、認知科學和應用等不同貢獻學科的啟發。與機器學習和控制論所有方面相關的新思想、設計備選方案、實現和案例研究屬于IJMLC的范圍。該期刊將涵蓋的主要研究領域包括：用于系統間交互建模的機器學習支持系統環境交互發現的模式識別技術系統環境交互控制生物和生物激發系統中的生物化學相互作用學習改進系統間通信方案