Exploring 10 601 Machine Learning Spring 2015 Lecture 12
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- Topics: bias-variance tradeoff, introduction to graphical models, conditional independence
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- Topics: support vector
- Topics: Logistic regression and its relation to naive Bayes, gradient descent
- Topics: additional practice
In-Depth Information on 10 601 Machine Learning Spring 2015 Lecture 12
Topics: inference in graphical models, d-separation, conditional independence Topics: principal component analysis (PCA), dimensionality reduction, kernel PCA Topics: inference in graphical models, expectation maximization (EM) Topics: Octave tutorial, Gaussian/normal distribution, maximum likelihood estimation (MLE), maximum a posteriori (MAP)
Topics: boosting, weak vs strong PAC
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