6.008 Introduction to Inference

Fall 2016

New in Fall 2016: 6.008 can be used in place of 6.01, 6.02, 6.03 as Intro to EECS

Instructors: Polina Golland, Gregory W Wornell

This subject covers the analytical and computational fundamentals of extracting information from data. Such technologies are used in

  • machine learning
  • search and retrieval
  • data mining
  • computer vision and imaging
  • voice recognition
  • communication and compression
  • natural language processing
  • robotics and navigation
  • computational biology and bioinformatics
  • medical diagnosis
  • distributed sensing and monitoring
  • finance.

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).

Course Catalog Description

6.008 Introduction to Inference

Prereq: Calculus II (GIR) or permission of instructor
Units: 4-4-4, Institute Lab
Lecture: MW10 (32-155) Lab: R2-4, F10-12 (32-044) Recitation: TR1 (34-302) or TR2 (34-302)

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. Substantial computational laboratory component explores the concepts introduced in class in the context of realistic contemporary applications. Students design inference algorithms, investigate their behavior on real data, and discuss experimental results.

Degree Requirements

The requirements satisfied by 6.008 will change in the future as the department settles into the new undergraduate curriculum. The requirements specified in this section are true for the Fall 2016 offering of 6.008 only.

New in Fall 2016: 6.008 can be used in place of 6.01, 6.02, 6.03 as Intro to EECS (by petition).

EECS students in 6-2 program can use 6.008 as one of their foundation subjects (old curriculum: EE or CS, new curriculum: EECS).

All EECS students can use 6.008 as one of their elective EECS 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 2016. 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.

6.008 can also be used by MEng students as one of their restricted elective subjects.

As usual, no double counting is allowed.

Header and AUS Subjects

The recently introduced 6.036 Introduction to Machine Learning is a course that can be taken by students interested in pursuing inference and machine learning further, and can be used by students instead of 6.034 to satisfy the CS header requirements. 6.008 is not a prerequisite for 6.036, but the two subjects make a natural sequence for students interested in the topics of inference and machine learning.

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.