Media Summary: Topics: overview of topics that may tested on Topics: classification, naive Bayes, introduction to maximum likelihood estimation (MLE), and maximum a posteriori estimation ... Topics: course logistics, high-level overview of
10 701 Machine Learning Fall 2014 Midterm 2 Review - Detailed Analysis & Overview
Topics: overview of topics that may tested on Topics: classification, naive Bayes, introduction to maximum likelihood estimation (MLE), and maximum a posteriori estimation ... Topics: course logistics, high-level overview of Topics: probabilistic modeling, graphical models, Gaussian mixture models, expectation maximization (EM) Lecturer: Abu ... Topics: Practice working with probability distributions involving linear algebra and matrix calculus Lecturer: Anthony Platanios ... For more information about Stanford's Artificial Intelligence professional and graduate programs, visit:
Topics: analysis of boosting, introduction to graphical models Lecturers: Aarti Singh and Geoff ... Topics: introduction to optimization and convexity, gradient descent, backtracking line search Lecturer: Anthony Platanios ... Topics: clustering, hierarchical clustering methods, k-means, mixture of Gaussians Lecturer: Aarti Singh ... Topics: hidden Markov model (HMM), belief propagation, junction tree algorithm Lecturer: Geoff Gordon ...
