15.096 Prediction: Machine Learning and Statistics
Gives a practical background and theoretical foundation to machine learning algorithms and Bayesian analysis. Includes an overview of the top ten algorithms in data mining. Covers frameworks for knowledge discovery, a unified view of support vector machines, AdaBoost and regression based on regularized risk minimization; generalization bounds from statistical learning theory based on covering numbers, VC dimension, and the margin theory; as well as basic Bayesian analysis and notes on the history of machine learning and statistics.
This class has no prerequisites.
15.096 will not be offered this semester. It will be available in the Spring semester, and will be instructed by C. Rudin.
This class counts for a total of 12 credits. This is a graduate-level class.
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