Due to the proliferation of mobile devices and services, the scale of multiple-input-multiple-output (MIMO) communication systems is getting larger and larger and can be massive in future wireless networks. This results in significant increases in hardware cost and power consumption. Recently, low-resolution analog-to-digital converters (ADCs) have been considered as a practical solution for reducing hardware cost and power consumption in MIMO systems. This is because low-resolution ADCs have simple hardware architectures as well as very low power consumption. However, the severe nonlinearity of low-resolution ADCs causes significant distortions in the received signals and therefore makes signal processing tasks such as channel estimation and data detection much more challenging compared to those in high-resolution systems. Motivated by the fact that machine learning is very powerful in solving non-linear problems, this dissertation exploits machine learning to develop low-complexity yet efficient and robust algorithms for channel estimation and data detection in MIMO systems with low-resolution ADCs.