Deep learning is only a subfield of machine learning. Other techniques in machine learning include decision trees, reinforcement learning, clustering, dimensionality reduction, bayesian inference, or even evolutionary optimization. To provide an initial idea of machine learning more generally, this chapter discusses the application of machine learning methods beyond neural networks, emphasizing their use in dimensionality reduction, reduced order models, and model identification. Techniques such as singular value decomposition, principal components analysis, reduced order modeling, sparse identification of non-linear dynamical systems (SINDy), clustering, and support vector machines are covered. The focus is on demonstrating how these methods complement neural networks and simulation.