The last five-year period has seen a impressive revolution in the theory and application of  machine learning and big data. Topics of interest include, but are not limited to:

• Foundations, algorithms, models and theory of data science, including big data mining.

• Machine learning and statistical methods for big data.

• Machine Learning algorithms and models. Neural Networks and Learning Systems. Convolutional neural networks.

• Unsupervised, semi-supervised, and supervised  Learning.

• Knowledge Discovery. Learning Representations. Representation learning for planning and reinforcement learning.

• Metric learning and kernel learning. Sparse coding and dimensionality expansion. Hierarchical models. Learning representations of outputs or states.

• Multi-objective optimization. Optimization and Game Theory. Surrogate-assisted Optimization. Derivative-free Optimization.

• Big data Mining from heterogeneous data sources, including text, semi-structured, spatio-temporal, streaming, graph, web, and multimedia data.

• Big Data mining systems and platforms, and their efficiency, scalability, security and privacy.

• Computational optimization. Optimization for representation learning. Optimization under Uncertainty

• Optimization algorithms for Real World Applications. Optimization for Big Data. Optimization and Machine Learning.

• Implementation issues, parallelization, software platforms, hardware

• Big Data mining for modeling, visualization, personalization, and recommendation.

• Big Data mining for cyber-physical systems and complex, time-evolving networks.

• Applications in social sciences, physical sciences, engineering, life sciences, web, marketing, finance, precision medicine, health informatics, medicine and other domains.