6.008 Introduction to Inference
Fall 2020
Instructors: Gregory W Wornell, Lizhong Zheng
TAs: Varkey T Alumootil, Sule Kahraman, Jennifer Susan Tang
Lecture:
MW10
(Zoom)
Recitations: TR12 or TR1 or TR2 (for the first week of classes)
(Zoom)
Lab Hours: W6-9, R1-5 & 6-9, F10-12
(Zoom)
TA Office Hours: M3-5, T4-6, R5-6
(Zoom)
Lecturer Office Hours: W2-3 (Greg), M2-4 (Lizhong)
(Zoom)
Course Description:
Introduces probabilistic modeling for problems of inference and
machine learning from data, emphasizing analytical and
computational aspects. Distributions, marginalization,
conditioning, and structure; graphical and neural network
representations. Belief propagation, decision-making,
classification, estimation, and prediction. Sampling methods and
analysis. 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.
LAs: Tanner Bonner, Giray Kuru, Jeremy Ma, and Danielle
White
ACCESS TO THE WEBSITE is limited to the students enrolled in the course as LISTENERS or FOR CREDIT. Please talk to us in class about registering and getting access to the web site. You can download the general information handout by clicking on the "General Info" tab on the left, even if you are not enrolled in the course.
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