6.867 Machine Learning, Fall 2002

6.867 Machine Learning, Fall 2002

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...

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Bibliographic Details
Main Author: Jaakkola, Tommi S. (Tommi Sakari)
Other Authors: Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science
Format: Learning Object
Language:en-US
Published: 2002
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
Online Access:http://hdl.handle.net/1721.1/46320

Internet

http://hdl.handle.net/1721.1/46320

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