This course introduces some concepts of atomistic simulations and machine learning, and familiarizes students with some numerical methods which are essential to achieve the aim of this course, namely “learning machine learning”. Atomistic simulation methods are new tools that allow one to predict functional material properties such as structural, electronic and thermal properties from the chemical makeup of the material by solving the equations of motion in quantum and classical regimes, or by means of machine learning algorithms. In this crash course, students work toward mastering computational skills and artificial neural networks algorithm. Also, they learn how to work with a Library for Atomistic Modeling Environments i.e., FLAME. As the programing language, we will use mostly Fortran. This course has no prerequisites other than familiarity with Condensed Matter Physics/ Quantum Chemistry and a programming language e.g., Fortran or C++. In order to follow the course more efficiently, and perform the hands-on training, it is desired that students bring their own laptops to the class.