This school is addressed to doctorates and post-docs working in particle physics, with special emphasis on those working on LHCb, Belle II, Gaia, CTA or MAGIC. The School will be structured around two Kaggle-challenge-like projects, one in the domain of particle physics and one in astrophysics which highlight the two big groups of Machine Learning and Data Mining techniques: classification and separation. With around 5-6 hours of lessons per day, the basics of these techniques will be covered, both from the theoretical and from the practical (hands-on) point of view. Two pieces of software, covering most needs, will be introduced and students will be free to use any of them for tackling the projects. Additional time will be devoted to covering some other innovative techniques. The school includes the development of a hands-on assignment where the students (in teams of 2-3) will have to choose one of two proposed projects and work on it during the week applying the techniques that they have learned in the classroom. The first days will be more theory heavy, with less time for working, and towards the end the students will have more hands-on sessions and more time to work on these projects. On the last day, each student (or group of students) will present their work that will be ranked; a prize will be given to the best work.