This document outlines an agenda for a presentation on deep learning and its applications in non-cognitive domains. The presentation is divided into three parts: an introduction to deep learning theory, applying deep learning to non-cognitive domains in practice, and advanced topics. The introduction covers neural network architectures like feedforward, recurrent, and convolutional networks. It also discusses techniques for improving training like rectified linear units and skip connections. The practice section will provide hands-on examples in domains like healthcare and software engineering. The advanced topics section will discuss unsupervised learning, structured outputs, and positioning techniques in deep learning.