Machine learning, in particular deep learning, has become one of the most significant propellers in many data-intensive fields, bringing about performance break-through in computer vision, natural language processing, and many other applications related to artificial intelligence. This tutorial aims at introducing machine and deep learning to an audience of circuits and systems engineers and researchers, with a unique focus on learning paradigms and techniques that are useful for big-data processing in edge and cloud computing systems. The tutorial will start with a selfcontained presentation of foundations of machine learning, covering topics including typical models and learning paradigms (supervised, unsupervised, reinforcement learning, etc.). Then deep learning will be discussed, with a focus on convolutional architectures for their wide range of applications. With these preparations, the tutorial will present unique challenges and potential solutions in deploying large-scale learning paradigms in edge-cloud computing systems. Topics to cover on this regard include network pruning (e.g., for fitting large deep networks on small edge devices), transfer learning (e.g., leveraging the cloud to mentor an edge device in learning), and energy-efficient deep learning implementations (e.g., binary neural networks, FPGA implementations, etc.). Lastly, several specific applications will be used to illustrate some of the discussed learning paradigms and/or deployment strategies. From this tutorial, a participant will be able to not only learn foundational knowledge in machine/deep learning but also gain a good understanding on how to deploy leading deep networks for particular applications in edge-cloud computing systems.