Call For Papers (Download PDF)

Scope of the Conference:

We encourage submissions of high quality research papers on all topics in the general area of machine learning and its applications. Topics of interest include, but are not limited to, the following areas:

•General Machine Learning (e.g., statistical learning, reinforcement learning, supervised learning, unsupervised learning, clustering, hybrid learning, federated learning, online and incremental learning, ranking, feature selection, few-shot learning, evolutionary learning, etc.)

•Deep Learning (neural network models, deep reinforcement learning, etc.)

•Learning Theory (game theory, statistical learning theory, computational learning theory, plausible reasoning theory and models, etc.)

•Machine Learning performance and optimization (network architectures search, pruning, quantization, learning low capacity devices, scalability of learning algorithms, system, performance, offloading, distributed and parallel learning, etc.)

•Probabilistic Inference (Bayesian methods, graphical models, Monte Carlo methods, etc.)

•Trustworthy Machine Learning (security, privacy, adversarial learning, etc.)

•Applications (gaming, problem solving, virtual environments, industry, manufacturing, homeland security, medicine, bioinformatics and system biology, healthcare, neuroscience, economics, business, social good, web, mobile data, time series data, multimedia data, natural language processing, data mining, information retrieval, knowledge discovery, etc.)

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.

Paper Submission Formats:

Papers submitted for reviewing should conform to IEEE specifications with maximum length of 8 pages. Manuscript templates can be downloaded from IEEE website ( All submissions must be anonymized and may not contain any information with the intention or consequence of violating the double-blind reviewing policy, including (but not limited to) citing previous works of the authors or sharing links in a way that can infer any author’s identity or institution, actions that reveal the identities of the authors to potential reviewers.


Please follow the link ( for initial submission and updates.

Journal Publication:

A short list of presented papers will be selected so that revised and extended versions of these papers will be published in 1) special issue on Application of Machine Learning Techniques for Sensing and Imaging of Sensors of MDPI Sensors; 2) Journal of Machine Learning Theory, Applications and Practice (JMLTAP) by River Publishers