Availability of labelled data for supervised learning is a major problem for narrow AI in current day industry. In imaging, the task of semantic segmentation (pixel-level labelling) requires humans to provide strong pixel-level annotations for millions of images and is difficult when compared to the task of generating weak image-level labels. Unsupervised representation learning along with semi-supervised classification is essential when strong annotations are hard to come by. This talk will introduce you to the techniques available in unsupervised learning and semi-supervised learning with specific focus on brain tumor segmentation from MRI using Stacked De-noising Auto-Encoders [(SDAEs)](http://www.jmlr.org/papers/volume11/vincent10a/vincent10a.pdf), which achieved competitive results in comparison with purely supervised Convolutional Neural Networks [(CNNs)](http://cs231n.github.io/convolutional-networks/), and highlight recent breakthroughs in AI with Generative Adversarial Networks [(GANs)](http://blog.aylien.com/introduction-generative-adversarial-networks-code-tensorflow/) for computer vision. Although the focus is on medical imaging, the techniques will be presented in a domain agnostic manner and can be easily translated for other sectors of deep learning.