The tremendous success of machine learning and deep learning in complicated problems has been accomplished for last ten years. Starting from the AlphaGo from Google deepmind to notoriously well-known problems in image classification, image recognition, voice regeneration, text generation, and speech recognition, they cannot be modelled and resolved by a classical scientific methodologies and numerical algorithms but smart neural networks with recently developed methodologies such as generative adversary network, variational autoencoder, and recurrent neural network can provide incredibly plausible solutions. It is certainly great success in engineering and application field. However, in machine learning (data-driven modelling ), there are some unsettled factors which cannot be completely understood in a modeling point of view. It is strongly related to fidelity and safety in many applications. In the following sessions, we aim to understand the nature of machine learning, not to only review fancy examples which work very well in applications. Three groups in South Korea which are dedicated to industrial mathematics (industrial & mathematical data analytics research center in Seoul national university, industrial mathematics center on big data in Pusan national university, innovation center for industrial mathematics, national institute for mathematical sciences) will organize the sessions for industrial and applied mathematics in machine learning. The other three sessions are organized by Dr. Kab Seok Kang (Max-Planck Institute for Plasma Physics, Germany), Dr. Young Saeng Park (University of Warwick, UK), and Dr. Sogkyun Kim (Rolls-Royce plc, UK). The topics are high performance computing, Advanced Automation for Industry 4.0, and big data in aerospace & automotive engineering.