15.096 Prediction: Machine Learning and Statistics


Class Info

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 be offered this semester (Spring 2018). It is instructed by .

This class counts for a total of 12 credits. This is a graduate-level class.

You can find more information at the MIT + 15.096 - Google Search site or on the 15.096 Stellar site.

MIT 15.096 Prediction: Machine Learning and Statistics Related Textbooks
MIT 15.096 Prediction: Machine Learning and Statistics On The Web
MIT + 15.096 - Google Search

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