Many of the existing human interfaces require input from the human during training, in the form of labels, comments, and parameter guidance. In many of those applications, users only have implicit knowledge, and thus unable to label a large training dataset. Furthermore, for many practical applications, users have their own explicit goal of  their task which often could be translated as a stopping criterion of "performance is good enough, no further parameter tuning is necessary". The goal of this class is to explore the latest machine learning algorithms that allow humans to be part of the training process.

In addition to learning how to use the state-of-the-art machine learning framework, we will study the latest literature on machine learning algorithms that outlines new possibilities of human-machine symbiosis. Much of the course will be focused on building towards and executing a final research project that involves a machine learning algorithm with a human in the loop.