Many of the most successful inference and machine learning algorithms arise out of probabilistic modeling and analysis. If you want to learn the fundamentals of this discipline and see some of what you can do with it, this subject is the place to start.
6.008 provides a solid foundation for more advanced subjects that build on this framework of reasoning. As such, the subject is targeted at (and likely to strongly appeal to) students both across and beyond Course 6 (EECS).
As a new subject in our curriculum, we will engage in considerable experimentation with content and pedagogy. While this is an exciting and important part of course development, it also means that the subject will be rougher and more raw than a subject that has been offered many times. As a result, this offering of 6.008 will especially appeal to students who, as part of taking the subject, relish the role of beta-tester and are eager to contribute to the subject's formative development through their participation.
General Information and Syllabus
Frequently Asked Questions
Recitation: TR11 or TR1 or TR2
Introduction to 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. Introduction to asymptotic analysis and information measures, and applications.
EECS students in 6-2 program can use 6.008 as one of their EE or CS foundation subjects. All EECS students can use 6.008 as one of their math elective or free elective subjects.
All EECS students may petition to take 6.008 instead of 6.042 as one of their math elective subjects and to use it as a prerequisite for more advanced subjects that require 6.042. NOTE: the petition must be filed and approved before the add date in Fall 2015. The goal is to verify that you either have sufficient background in constructing formal proofs (an essential component of 6.042 that more advanced subjects rely on) or that you are committed to acquiring such background on your own. More information can be found in Frequently Asked Questions.
6.008 can also be used by MEng students as one of their restricted elective subjects. As usual, no double counting is allowed.
We currently offer several graduate subjects in inference and machine learning that can be taken as AUS subjects, including 6.437 Inference and Information, 6.438 Algorithms for Inference, and 6.867 Machine Learning.