The talk starts with introducing connections between signal processing and machine learning in two applications: a) speech processing b) wireless communication. First, we explore some concepts on linear algebra, the core connection between the two topics and some basic DSP concepts. Then, we look at the first application, speech processing and explore basic ideas like cepstrum and mel-frequency cepstral coefficients (MFCC) and then introduce wavelets and its connections to machine learning. Then, we look at another application: wireless communication, and explore connections between traditional estimation and detection methods and their machine learning counterparts. The objective of the talk is to connect signal processing and machine learning and explore their correlations.