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. Computational laboratory component explores the concepts introduced in class in the context contemporary applications. Students design inference algorithms, investigate their behavior on real data, and discuss experimental results.

This class has no prerequisites.

6.008 will be offered this semester (Fall 2019). It is instructed by G. Wornell.

This class counts for a total of 12 credits.

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