6.008 Introduction to Inference


Class Info

Introduces probabilistic modeling for problems of inference and machine learning from data, emphasizing analytical and computational aspects. Distributions, marginalization, conditioning, and structure; graphical representations. Belief propagation, decision-making, classification, estimation, and prediction. Sampling methods and analysis. Introduces asymptotic analysis and information measures. Substantial computational laboratory component explores the concepts introduced in class in the context of realistic contemporary applications. Students design inference algorithms, investigate their behavior on real data, and discuss experimental results.

This class has 18.02 as a prerequisite.

6.008 will be offered this semester (Fall 2017). It is instructed by P. Golland and G. W. Wornell.

Lecture occurs 10:00 AM to 11:00 AM on Mondays and Wednesdays in 32-155.

This class counts for a total of 12 credits.

In the Fall 2015 Subject Evaluations, 6.008 was rated 6.4 out of 7.0. You can find more information at the 6.008 site or on the 6.008 Stellar site.

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6.008
Tags
eecs inference machine subjects polina golland machine learning cs

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