Introduction to 10 701 Machine Learning Fall 2014 Lecture 12
Let's dive into the details surrounding 10 701 Machine Learning Fall 2014 Lecture 12. Topics: kernel density estimation, k-nearest neighbors, local regression, introduction to spatially adaptive nonparametric methods ...
10 701 Machine Learning Fall 2014 Lecture 12 Comprehensive Overview
Gaussian Processes, Part 1 Introduction to Topics: principal component analysis (PCA), deep Topics:
Topics: hidden Markov models, forward-backward algorithm, Viterbi algorithm for finding the most probable state sequence, EM ...
Summary & Highlights for 10 701 Machine Learning Fall 2014 Lecture 12
- Topics: Newton's method, backtracking line search, constrained optimization, stochastic gradient descent, density estimation ...
- Topics: analysis of boosting, introduction to graphical models Lecturers: Aarti Singh and Geoff ...
- Topics: course logistics, high-level overview of
- Topics: clustering, hierarchical clustering methods, k-means, mixture of Gaussians
- Topics: d-separation, Bayes ball algorithm, factor graphs, Markov random fields
That wraps up our extensive overview of 10 701 Machine Learning Fall 2014 Lecture 12.
