Machine learning applications in high-energy physics Over the last years, machine learning tools have been successfully applied to a wealth of problems in high-energy physics. A typical example is the classification of physics objects. Supervised machine learning methods allow for significant improvements in classification problems by taking into account observable correlations and by learning the optimum selection from examples, e.g. from Monte Carlo simulations. Even more promising is the usage of deep learning techniques. Methods like deep convolutional networks might be able to catch features from low-level parameters which are not exploited by default cut-based methods. And generative models like the prominent GANs might eventually even be able to substitute Monte Carlo simulations. These ideas can be particularly beneficial for measurements in heavy-ion collisions, because of the very large multiplicities. Indeed, machine learning methods potentially perform much better in systems with a large number of degrees of freedom compared to cut-based methods. Moreover, many key heavy-ion observables are most interesting at low transverse momentum where the underlying event is dominant and the signal-to-noise ratio is quite low. In this seminar talk, basic machine learning concepts as well as advanced ideas will be presented. After a broader introduction to machine learning techniques, several examples - focused but not restricted on ALICE - will be discussed to introduce a variety of different applications.