Media Summary: Topics: error bounds for infinite hypothesis spaces, Vapnik–Chervonenkis (VC) dimension, Rademacher complexity Topics: clustering, hierarchical clustering methods, k-means, mixture of Gaussians Topics: plate notation in graphical models, introduction to
10 701 Machine Learning Fall 2014 Lecture 19 - Detailed Analysis & Overview
Topics: error bounds for infinite hypothesis spaces, Vapnik–Chervonenkis (VC) dimension, Rademacher complexity Topics: clustering, hierarchical clustering methods, k-means, mixture of Gaussians Topics: plate notation in graphical models, introduction to graphical models: factor graphs, Markov random fields, junction trees Note: interesting part starts at minute 4:30 due to slight ... Topics: course logistics, high-level overview of Topics: expectation maximization (EM), convergence of EM, principal component analysis (PCA)
Topics: analysis of boosting, introduction to graphical models Lecturers: Aarti Singh and Geoff ... Topics: polynomial regression, kernelized regression, Gaussian process (GP) regression Topics: kernel perceptron, kernel engineering, support vector Topics: hidden Markov model (HMM), belief propagation, junction tree algorithm Topics: overview of topics that may tested on exam, open Q&A Subscribe our channel for more Engineering
Topics: overview of topics tested on exam, Q&A
