6.008 Introduction to Inference
Introduces probabilistic modeling for problems of inference and machine learning from data, emphasizing analytical and computational aspects. Distributions, marginalization, conditioning, and structure, including graphical and neural network 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 of 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 P. Golland.
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.
You can find more information at the 6.008 site.
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