Machine learning (ML) has become the de-facto approach for various scientific domains such as computer vision and natural language processing. Despite recent breakthroughs, machine learning has only made its way into the fundamental challenges in computer systems and networking recently. This article attempts to shed light on recent literature that appeals for machine learning-based solutions to traditional problems in computer systems and networking. To this end, we first introduce a taxonomy based on a set of major research problem domains. Then, we present a comprehensive review per domain, where we compare the traditional approaches against the machine learning-based ones. Finally, we discuss the general limitations of machine learning for computer systems and networking, including lack of training data, training overhead, real-time performance, and explainability, and reveal future research directions targeting these limitations.