This mini-tutorial provides an introduction to the recent development of stochastic approximation (SA) scheme. The first part introduces the essential foundation of the SA scheme as a general device for locating the roots of mean field functions. The presenter then showcases how the SA scheme can be applied to examples in signal processing and machine learning, such as compressed training, online expectation maximization, reinforcement learning, performative prediction, etc., that go beyond the use of gradient updates.