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; 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 18.02 as a prerequisite.
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.
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