Exploring 10 601 Machine Learning Spring 2015 Lecture 26
Exploring 10 601 Machine Learning Spring 2015 Lecture 26 reveals several interesting facts.
- Topics: reinforcement
- Topics: never-ending
- Topics: inference in graphical models, d-separation, conditional independence
- Topics: application of naive Bayes to document classification, Gaussian naive Bayes and application to brain imaging
- Topics: support vector
In-Depth Information on 10 601 Machine Learning Spring 2015 Lecture 26
Topics: deep learning, restricted Boltzmann machines, privacy in Topics: Logistic regression and its relation to naive Bayes, gradient descent Topics: support vector Topics: Octave tutorial, Gaussian/normal distribution, maximum likelihood estimation (MLE), maximum a posteriori (MAP)
Topics: linear regression, logistic regression, gradient descent
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