Media Summary: Topics: clustering, hierarchical clustering methods, k-means, mixture of Gaussians Graphical models: junction trees, belief propagation. Note that the first Topics: course logistics, high-level overview of

10 701 Machine Learning Fall 2014 Lecture 20 - Detailed Analysis & Overview

Topics: clustering, hierarchical clustering methods, k-means, mixture of Gaussians Graphical models: junction trees, belief propagation. Note that the first Topics: course logistics, high-level overview of Topics: error bounds for infinite hypothesis spaces, Vapnik–Chervonenkis (VC) dimension, Rademacher complexity Topics: overview of topics that may tested on exam, open Q&A Topics: overview of topics tested on exam, Q&A

Topics: hidden Markov models, forward-backward algorithm, Viterbi algorithm for finding the most probable state sequence, EM ...

Photo Gallery

10-701 Machine Learning Fall 2014 - Lecture 20
Machine Learning 10-701 Lecture 20, Exponential Families, Clustering
10-701 Machine Learning Fall 2013 Lecture 20
Lecture 20
10-701 Lecture 20: Learning HMMs
10-701 Machine Learning Fall 2014 - Lecture 1
10-701 Machine Learning Fall 2014 - Lecture 19
10-701 Machine Learning Fall 2014 - Midterm review
10-701 Machine Learning Fall 2014 - Midterm 2 review
10-701 Machine Learning Fall 2014 - Recitation 10
Sponsored
View Detailed Profile
10-701 Machine Learning Fall 2014 - Lecture 20

10-701 Machine Learning Fall 2014 - Lecture 20

Topics: clustering, hierarchical clustering methods, k-means, mixture of Gaussians

Machine Learning 10-701 Lecture 20, Exponential Families, Clustering

Machine Learning 10-701 Lecture 20, Exponential Families, Clustering

Introduction to

10-701 Machine Learning Fall 2013 Lecture 20

10-701 Machine Learning Fall 2013 Lecture 20

Graphical models: junction trees, belief propagation. Note that the first

Lecture 20

Lecture 20

Description.

10-701 Lecture 20: Learning HMMs

10-701 Lecture 20: Learning HMMs

... input data from

Sponsored
10-701 Machine Learning Fall 2014 - Lecture 1

10-701 Machine Learning Fall 2014 - Lecture 1

Topics: course logistics, high-level overview of

10-701 Machine Learning Fall 2014 - Lecture 19

10-701 Machine Learning Fall 2014 - Lecture 19

Topics: error bounds for infinite hypothesis spaces, Vapnik–Chervonenkis (VC) dimension, Rademacher complexity

10-701 Machine Learning Fall 2014 - Midterm review

10-701 Machine Learning Fall 2014 - Midterm review

Topics: overview of topics that may tested on exam, open Q&A

10-701 Machine Learning Fall 2014 - Midterm 2 review

10-701 Machine Learning Fall 2014 - Midterm 2 review

Topics: overview of topics tested on exam, Q&A

10-701 Machine Learning Fall 2014 - Recitation 10

10-701 Machine Learning Fall 2014 - Recitation 10

Topics: hidden Markov models, forward-backward algorithm, Viterbi algorithm for finding the most probable state sequence, EM ...