This course will introduce modern computational methods for materials science, including artificial intelligence (AI), machine learning (ML) and density functional theory (DFT). Both AI/ML and DFT are becoming standard tools in chemistry, physics, and materials science. Deep learning specifically involves linking input data (features) with output data (labels) through a neural network. Neural networks are capable of approximating any function. A typical example is the relationship between a material's structure and its properties. Conversely, DFT is a computational method used to analyze the electronic structure of atoms, molecules, and materials. It is based on quantum mechanics and provides valuable insights into the properties and behavior of various materials. Both DFT and AI/ML have their own strengths and applications, and they can be combined. Depending on the engineering field and the specific problem, these methods can be relevant. Therefore, AI/ML and DFT are valuable tools for students who will become engineers and scientists.