The next generation of radio telescopes will have unprecedented sensitivity and time-resolution offering exciting new capabilities in time-domain science. However, this will result in very large numbers of pulsar and transient event candidates and the associated data rates will be technically challenging in terms of data storage and signal processing. Automated detection and classification techniques are therefore required and must be optimized to allow high-throughput data processing in real time. In this paper we provide a summary of the emerging machine learning techniques being applied to this problem.