Exploring 10 701 Machine Learning Fall 2014 Lecture 14
Exploring 10 701 Machine Learning Fall 2014 Lecture 14 reveals several interesting facts.
- Topics: perceptron, linear programming, "perceptron algorithm"
- Topics: course logistics, high-level overview of
- Topics: principal component analysis (PCA), deep
- Topics: expectation maximization (EM), convergence of EM, principal component analysis (PCA)
- Topics: logistic regression, generative vs discriminative classifiers, analysis of perceptron algorithm Lecturers: Aarti Singh and ...
In-Depth Information on 10 701 Machine Learning Fall 2014 Lecture 14
Topics: analysis of boosting, introduction to graphical models Lecturers: Aarti Singh and Geoff ... Topics: clustering, hierarchical clustering methods, k-means, mixture of Gaussians Topics: graphical models, variable elimination, Bayesian networks, independence relations in graphical models Topics:
Topics: kernel density estimation, k-nearest neighbors, local regression, introduction to spatially adaptive nonparametric methods ...
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