Media Summary: Topics: kernel perceptron, kernel engineering, support vector Topics: Practice working with probability distributions involving linear algebra and matrix calculus Topics: linear regression, least squares, polynomial regression
10 701 Machine Learning Fall 2014 Lecture 7 - Detailed Analysis & Overview
Topics: kernel perceptron, kernel engineering, support vector Topics: Practice working with probability distributions involving linear algebra and matrix calculus Topics: linear regression, least squares, polynomial regression Topics: course logistics, high-level overview of Topics: probabilistic modeling, graphical models, Gaussian mixture models, expectation maximization (EM) Topics: overview of topics that may tested on exam, open Q&A
Topics: reproducing kernel Hilbert space, kernel perceptron algorithm and analysis Topics: clustering, hierarchical clustering methods, k-means, mixture of Gaussians Topics: hidden Markov model (HMM), belief propagation, junction tree algorithm Topics: error bounds for infinite hypothesis spaces, Vapnik–Chervonenkis (VC) dimension, Rademacher complexity Topics: Newton's method, backtracking line search, constrained optimization, stochastic gradient descent, density estimation ...
