10-418 Machine Learning for Structured Data
12 units, Carnegie Mellon University
A key challenge in machine learning is that of structured prediction taking unstructured data as input and producing a structured output. Structured prediction problems abound throughout application areas such as natural language processing, speech processing, computational biology, computer vision, healthcare, and many others. In this course, we will study modern approaches to structured prediction building on probabilistic graphical models, deep learning, and search. The course will focus on three key aspects models, inference, and learning. The models we consider will focus on both generative and discriminative models such as Bayesian networks, Markov random fields MRFs, conditional random fields CRFs, and deep neural networks including convolutional neural networks CNNs and recurrent neural networks RNNs as well as hybrids of graphical models and neural networks. The course will explore approaches to exact and approximate inference junction tree algorithm, approximate marginal inference by Markov chain Monte Carlo MCMC and variational methods, approximate MAP inference by integer linear programming ILP and search. We will explore unsupervised, semisupervised, and supervised learning using different formulations of the learning problem MLE, Bayesian inference, structured perceptron, M3Ns, learning to search, and autoencoders. Covered applications will include machine translation, speech recognition, DNA sequence analysis, scene understanding, medical diagnosis. This course is crosslisted as 10418 and 10618 students registered for 10618 will do a course project.
Prerequisites: 10-301 (min C) or 10-315 (min C) or 10-715 (min C) or 10-601 (min C) or 10-701 (min C) or 10-401 (min C)
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