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6.3800 Introduction to Inference

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Prereq: Calculus II (GIR) or permission of instructor
Units: 4-4-4
Lecture: MW10 (32-141) Lab: TBA Recitation: TR1 (2-105) or TR2 (2-105) +final

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.
P. Golland
No required or recommended textbooks