Proceedings of the Machine Learning and nuclear physics Meeting

The goal of this meeting was to inform, exchange, discuss on Machine Learning technologies in particular for applications in Nuclear Physics. The meeting is divided in four sessions: 1 - General information (comprehensive overview, possibilities, issues and technologies to be presented); 2 - Practical examples (presentation of concrete applications); 3 - Feedback/expectations from the community (gathering community needs, providing hints to start with); 4 - Round table and discussion. This document brings together the available presentations: 1 - Meeting introduction; 2 - Introduction lecture to neural networks with description of some popular algorithms; 3 - Review of machine learning methods used in particle physics; 4 - Use of machine learning in intraoperative isotope imaging; 5 - Machine Learning for gamma-neutron discrimination (studies); 6 - Machine Learning for gamma-neutron discrimination (implementation); 7 - Artificial intelligence and machine learning for reactor and electronuclear studies; 8 - Self-learning algorithm on current signals from silicon detectors; 9 - Graphics Processing Unit (GPU), Machine Learning and Online; 10 - Machine Learning at ILL; 11 - Tracking particles in an active target; 12 - learning to unmix in gamma-ray spectrometry; 13 - Taming nuclear complexity using deep neural networks; 14 - NPB (Non Parametric Bootstrap), MCMC (Markov-Chain-Monte-Carlo), GPE (Gaussian Process Emulator) and other funny acronyms