Exploring 10 701 Machine Learning Fall 2014 Lecture 22
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- Topics: kernel density estimation, k-nearest neighbors, local regression, introduction to spatially adaptive nonparametric methods ...
- Topics: optimization, gradient descent, Newton's method, convergence analysis
- Topics: graphical models, variable elimination, Bayesian networks, independence relations in graphical models
- Topics: perceptron, linear programming, "perceptron algorithm"
- Topics: plate notation in graphical models, introduction to
In-Depth Information on 10 701 Machine Learning Fall 2014 Lecture 22
Topics: principal component analysis (PCA), deep decision trees, bagging, discriminative v. generative. Topics: course logistics, high-level overview of Topics: expectation maximization (EM), convergence of EM, principal component analysis (PCA)
Topics: Deep
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