MIT OpenCourseWare

Browse materialsMIT 6.867 · Fall 2006

Machine Learning

Subjects: Artificial Intelligence · Machine Learning · Science · Statistics

Art: Palmer, Edward, 1803-1872, Figures 113-123. Plate Electrical Machines, 1840 · Public domain

Instructors: Rohit Singh, Prof. Tommi Jaakkola, Ali Mohammad
Term: Fall 2006
Level: Graduate
What's inside: 1 lecture note · 1 reading · 67 other files · 5 web pages
Materials open: MIT OCW

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About this course

Source: MIT OpenCourseWare 6.867 Machine Learning, Fall 2006, Rohit Singh, Prof. Tommi Jaakkola, Ali Mohammad — original course page on ocw.mit.edu ↗. License: CC BY-NC-SA 4.0. Study order: MIT's own listing order, section by section. Taken from MIT's published course files, downloaded from ocw.mit.edu on September 23, 2026. How RedLectern is built.

Materials are grouped the way MIT organized them. A verified, course-wide order has not been established here.

Has MIT solutions 15 files in this course are named by MIT as solutions.

Before you start

Prerequisites are not listed here. Check MIT's own syllabus for this course.

How this course was organized

What's here, and what isn't

A lecture sequence of 24 items is confirmed. Assignment and exam materials exist too, but MIT's own order for them could not be verified, so they're shown as materials below rather than a sequence.

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MIT's page format for this item wasn't recognized clearly enough to place it safely. (6 items)
Referenced in a table, but without MIT's own verified order, so it isn't used to claim a sequence. (2 items)

Materials by section

Grouped by section, as MIT organized them. Not sequenced or ordered.

Readings

Short tutorial on Lagrange multipliersPDF Download

Lecture Notes

23 files

Assignments

23 files

Exams

17 files

Tools

MIT OpenCourseWare, Rohit Singh, Prof. Tommi Jaakkola, Ali Mohammad, 6.867 Machine Learning, Fall 2006.

License: CC BY-NC-SA 4.0 · Edition: Fall 2006 · Open the original course on OCW ↗

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Machine Learning

Before you start

What's here, and what isn't

Not included here

Materials by section

Readings

Lecture Notes

Assignments

Exams

Tools

Lecture 1: Introduction, linear classification, perceptron update rule ( PDF )

Lecture 2: Perceptron convergence, generalization ( PDF )

Lecture 3: Maximum margin classification ( PDF )

Lecture 4: Classification errors, regularization, logistic regression ( PDF )

Lecture 5: Linear regression, estimator bias and variance, active learning ( PDF )

Lecture 6: Active learning (cont.), non-linear predictions, kernals ( PDF )

Lecture 7: Kernal regression, kernels ( PDF )

Lecture 8: Support vector machine (SVM) and kernels, kernel optimization ( PDF )

Lecture 9: Model selection ( PDF )

Lecture 10: Model selection criteria ( PDF )

Lecture 11: Description length, feature selection ( PDF )

Lecture 12: Combining classifiers, boosting ( PDF )

Lecture 13: Boosting, margin, and complexity ( PDF )

Lecture 14: Margin and generalization, mixture models ( PDF )

Lecture 15: Mixtures and the expectation maximization (EM) algorithm ( PDF )

Lecture 16: EM, regularization, clustering ( PDF )

Lecture 17: Clustering ( PDF )

Lecture 18: Spectral clustering, Markov models ( PDF )

Lecture 19: Hidden Markov models (HMMs) ( PDF )

Lecture 20: HMMs (cont.) ( PDF )

Lecture 21: Bayesian networks ( PDF )

Lecture 22: Learning Bayesian networks ( PDF )

Lecture 23: Probabilistic inference Guest lecture on collaborative filtering ( PDF )

Lecture 24: Current problems in machine learning, wrap up