Exploring 10 601 Machine Learning Spring 2015 Lecture 18
Exploring 10 601 Machine Learning Spring 2015 Lecture 18 reveals several interesting facts.
- Topics: kernel methods, margin, kernelizing a
- Topics: bias-variance tradeoff, introduction to graphical models, conditional independence
- Topics: wrap-up of semi-supervised
- Lecture 18
- Topics: generalization error of Adaboost, margin, perceptron algorithm
In-Depth Information on 10 601 Machine Learning Spring 2015 Lecture 18
Topics: support vector Topics: semi-supervised Topics: support vector Topics: Logistic regression and its relation to naive Bayes, gradient descent
Topics: inference in graphical models, expectation maximization (EM)
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