'Mixture models' presentation slideshows

Pascal Grand Challenge

1.06k views • 81 slides

Predictively Modeling Social Text

Predictively Modeling Social Text. William W. Cohen Machine Learning Dept. and Language Technologies Institute School of Computer Science Carnegie Mellon University

842 views • 73 slides

Statistical Machine Learning and Computational Biology

Statistical Machine Learning and Computational Biology. Michael I. Jordan University of California, Berkeley November 5, 2007. Statistical Modeling in Biology. Motivated by underlying stochastic phenomena thermodynamics recombination mutation environment Motivated by our ignorance

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Pattern Recognition and Machine Learning

Institute of Empirical Research in Economics (IEW). Laboratory for Social & Neural Systems Research (SNS). Pattern Recognition and Machine Learning. Course schedule. Date Topic Chapter 13-10-2010 Density Estimation, Bayesian Inference 2

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Random variables, distributions and limit theorems

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Deep Learning

Deep Learning. Why?. Source: Huang et al., Communications ACM 01/2014.

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Lecture 17 Gaussian Mixture Models and Expectation Maximization

Lecture 17 Gaussian Mixture Models and Expectation Maximization. Machine Learning. Last Time. Logistic Regression. Today. Gaussian Mixture Models Expectation Maximization. The Problem. You have data that you believe is drawn from n populations

543 views • 34 slides

Clustering Methods

Clustering Methods. Moses Charikar Computer Science. Clustering. Partition data items into groups (clusters) Similar data items in the same group Dissimilar items in different groups unsupervised learning: groups not known apriori . Issues . Data representation

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High Performance Data Mining with Services on Multi-core systems

High Performance Data Mining with Services on Multi-core systems. Judy Qiu xqiu@indiana.edu , http://www.infomall.org/salsa Research Computing UITS , Indiana University Bloomington IN Geoffrey Fox, Huapeng Yuan, Seung-Hee Bae Community Grids Laboratory, Indiana University Bloomington IN

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Fields of Experts: A Framework for Learning Image Priors

2006. 7. 10 (Mon) Young Ki Baik, Computer Vision Lab. . Fields of Experts: A Framework for Learning Image Priors. Fields of Experts. References On the Spatial Statistics of Optical Flow Stefan Roth and Michael J. Black (ICCV 2005) Fields of Experts: A Framework for Learning Image Priors

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Image Localization and Geo-registration

Image Localization and Geo-registration. Embedded and High Performance Computing Karl Ni, karl.ni@ll.mit.edu MIT Lincoln Laboratory 22 September 2011.

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Lecture 7. Kernel Smoothing Methods

Lecture 7. Kernel Smoothing Methods. Instructed by Jinzhu Jia. Outline. Description of Kernel One-dimensional Kernel smoothing Selecting the Width of the Kernel Local Regression in R p Kernel Density Estimation and Classification. Nearest Neighbor Smoothing.

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On-demand learning-embedded benchmark assessment using classroom-accessible technology

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Track: Speech Technology

Track: Speech Technology. Kishore Prahallad Assistant Professor, IIIT-Hyderabad. Why do you need Speech Technology. 2001: A Space Odyssey (1968 Movie) Mission to Jupiter to contact Aliens Crew - five men and one of the latest generation of the HAL 9000 computers . HAL, Favorite actor .

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Automatic Image Annotation Using Group Sparsity

Automatic Image Annotation Using Group Sparsity. Shaoting Zhang 1 , Junzhou Huang 1 , Yuchi Huang 1 , Yang Yu 1 , Hongsheng Li 2 , Dimitris Metaxas 1 1 CBIM, Rutgers University, NJ 2 IDEA Lab, Lehigh University, PA. Introductions.

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Kernel Methods

Kernel Methods. Jong Cheol Jeong. Out line. 6.1 One-Dimensional Kernel Smoothers 6.1.1 Local Linear Regression 6.1.2 Local Polynomial Regression 6.2 Selecting the Width of Kernel 6.3 Local Regression in R p 6.4 Structured Local Regression Models in R p

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General Information

General Information. Course Id: COSC6342 Machine Learning Time: MO/WE 2:30-4p Instructor: Christoph F. Eick Classroom: SEC 201 E-mail: ceick@aol.com Homepage: http://www2.cs.uh.edu/~ceick/. What is Machine Learning?. Machine Learning is the

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RECITATION 2 APRIL 28

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Semi-supervised Learning

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