6.867 Machine Learning, Fall 2002
Name
6-867Fall-2002/OcwWeb/Electrical-Engineering-and-Computer-Science/6-867Machine-LearningFall2002/CourseHome/index.htm
Size
14.36 KB
Format
HTML
Checksum (MD5)
b60c93a6aa23438528ce9c6f28b16b4b
Author(s)
Jaakkola, Tommi S. (Tommi Sakari)
Alternative Title
Machine Learning
Date Issued
December 2002
Abstract
Principles, techniques, and algorithms in machine learning from the point of view of statistical inference; representation, generalization, and model selection; and methods such as linear/additive models, active learning, boosting, support vector machines, hidden Markov models, and Bayesian networks. From the course home page: Course Description 6.867 is an introductory course on machine learning which provides an overview of many techniques and algorithms in machine learning, beginning with topics such as simple perceptrons and ending up with more recent topics such as boosting, support vector machines, hidden Markov models, and Bayesian networks. The course gives the student the basic ideas and intuition behind modern machine learning methods as well as a bit more formal understanding of how and why they work. The underlying theme in the course is statistical inference as this provides the foundation for most of the methods covered.
Subjects
machine learning
perceptrons
boosting
support vector machines
Markov
hidden Markov models
HMM
Bayesian networks
statistical inference
regression
clustering
bias
variance
regularization
Generalized Linear Models
neural networks
Support Vector Machine
SVM
mixture models
kernel density estimation
gradient descent
quadratic programming
EM algorithm
orward-backward algorithm
junction tree algorithm
Gibbs sampling
Machine learning
MIT Department
Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science
Terms of Use
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