Icebreaker [1] is new research from MSR that is able to achieve state of the art performance on inference in which there is inherent missing data. Using mutual information, Icebreaker is able to suggest which values in the data to impute for maximum benefit. These notes are an amalgamation of information from various articles and tutorials including autoencoders, variational inference, variational autoencoders, the evidence lower bound, set based learning and finally leading to Icebreaker. References are provided whenever appropriate. There may be factual errors and typos in these notes. Please send them to the author.