NOTE: Please bring your laptop for hands-on exercises.
Brief description: These lectures will cover the fundamentals of machine learning theory and as well as it’s many practical applications in fields ranging from computer science to particle physics. The lectures will provide an introduction to basic and advanced machine learning methods, such as boost decision trees, neural networks, deep learning and others, and will contain hands-on examples that illustrate the methodology and available software for solving a variety of problems in these domains.
Speaker's short bio: Nikita Kazeev has graduated from Moscow Institute of Physics and Technology and from Data Science department of Yandex School of Data Analysis. He has extensive experience on applying machine learning methods at variety of problems of the LHCb experiment. He is a PhD student at Computer Science Department of the Higher School of Economics.
Link to the chat: https://gitter.im/cern_summer_school_2017/Lobby?utm_source=share-link&utm_medium=link&utm_campaign=share-link
