With the increasing popularity of applying machine learning for various artificial intelligence (AI) problems, a plethora of machine learning techniques are being developed. However, a critical limitation of conventional machine learning paradigms arises from the scarcity and expense of (labelled) data as popular deep learning methods often have millions of parameters and require thousands (if not millions) of samples to train an effective model. In this aspect, machine intelligence is considered inferior to human intelligence, as humans have the ability to learn rapidly from very limited data. This project explores new approaches to investigate the fundamental research problem of learning from small (labelled) data, called one-shot learning or few-shot learning, and will develop new algorithms and techniques to devise one-shot learning machines with human-like learning capabilities. The overall objective of this project is advance AI research – both in bringing AI closer to human intelligence and solving real-world problems.