10-807 Topics in Deep Learning
12 units, Carnegie Mellon University
Building intelligent machines that are capable of extracting meaningful representations from highdimensional data lies at the core of solving many AI related tasks. In the past few years, researchers across many different communities, from applied statistics to engineering, computer science and neuroscience, have developed deep hierarchical models models that are composed of several layers of nonlinear processing. An important property of these models is that they can learn useful representations by reusing and combining intermediate concepts, allowing these models to be successfully applied in a wide variety of domains, including visual object recognition, information retrieval, natural language processing, and speech perception. This is an advanced graduate course, designed for Masters and Ph.D. level students, and will assume a reasonable degree of mathematical maturity. The goal of this course is to introduce students to the recent and exciting developments of various deep learning methods. Some topics to be covered include restricted Boltzmann machines RBMs and their multilayer extensions Deep Belief Networks and Deep Boltzmann machines sparse coding, autoencoders, variational autoencoders, convolutional neural networks, recurrent neural networks, generative adversarial networks, and attentionbased models with applications in vision, NLP, and multimodal learning. We will also address mathematical issues, focusing on efficient largescale optimization methods for inference and learning, as well as training density models with intractable partition functions. Prerequisite ML 10701 or 10715, and strong programming skills.
Prerequisites: 10-701 (min C) or 10-715 (min C)
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