10-716 Advanced Machine Learning: Theory and Methods
12 units, Carnegie Mellon University
Advanced Machine Learning is a graduate level course introducing the theoretical foundations of modern machine learning, as well as advanced methods and frameworks used in modern machine learning. The course assumes that students have taken graduate level introductory courses in machine learning Introduction to Machine Learning, 10701 or 10715, as well as Statistics Intermediate Statistics, 36700 or 36705. The course treats both the art of designing good learning algorithms, as well as the science of analyzing an algorithms computational and statistical properties and performance guarantees. Theorems are presented together with practical aspects of methodology and intuition to help students develop tools for selecting appropriate methods and approaches to problems in their own research. We will cover advanced machine learning methods such as nonparametric and deep compositional approaches to density estimation and regression advanced theory such as fundamentals of clustering, classification, boosting theory and methods at the intersection of statistical and computational efficiency as well as vignettes of theoretical results on some hot topics such as robustness and explainability.
Prerequisites: (10-701 (min C) or 10-715 (min C)) and (36-705 (min C) or 36-700 (min C))
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