Numerical models are used in every engineering task, from conceptual design to optimization, control, and diagnostics. As the process becomes more complex, data driven models are a powerful tool that allows to quantify relationships between available data and observations, which forms the basis for machine learning. Image recognition, spam filtering, and predictive analytics are some examples of how we can use data driven models. This course provides a simple introduction to fundamental techniques for dimensionality reduction, classification, and regression, which can be applied to all types of engineering problems.