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Carnegie Mellon Machine Learning Lunch seminar

Carnegie Mellon Machine Learning Lunch seminar

20 Videos · Jan 21, 2008

About

The Machine Learning lunch is a weekly seminar which has the goal of bringing together the different people at CMU working on related fields to discuss their work. In the past a broad range of topics has been discussed: reinforcement learning, machine learning in general, statistical AI, statistical learning theory, robot learning, text learning, etc. The talks have always been enjoyable and have ranged from quite informal to formal conference style talks. It is also a great forum to practice conference talks, bounce around new ideas and for guests from other universities and industry to speak. Currently the talks are sponsored by //**MLD - the Machine Learning Department of the School of Computer Science.

The goal of MLD is slightly broader than that of these talks - it brings together the many departments working on similar topics at CMU. The series has been going on for quite a few years. In earlier days it was called the Reinforcement Learning Lunch because of the emphasis on reinforcement learning. As the topics broadened, the name was changed to the Machine Learning Lunch.

Organizing committee: Amr Ahmed, Polo Chau, Steve Hanneke, Sue Ann Hong, Nathan Ratliff


{{http://l.yimg.com/a/i/ww/beta/y3.gif}} This lecture series is being kindly sponsored by Yahoo! Academic Relations

Videos

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01:00:27

Large Scale Scene Matching for Graphics and Vision

James Hays

calendar icon Mar 29, 2009 6279 views

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Efficient Parallel Learning of Linear Dynamical Systems on SMPs

Lei Li

calendar icon Mar 29, 2009 4527 views

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24:31

Weighted Graphs and Disconnected Components: Patterns and a Generator

Mary McGlohon

calendar icon Mar 29, 2009 5434 views

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27:03

Partially Observed Maximum Entropy Discrimination Markov Networks

Jun Zhu

calendar icon Jan 15, 2009 5672 views

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26:24

Local Minima Free Parameterized Appearance Models

Minh Hoai Nguyen

calendar icon Jan 15, 2009 6887 views

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58:44

Probabilistic Decision-Making Under Model Uncertainty

Joelle Pineau

calendar icon Jan 15, 2009 12139 views

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01:05:50

Some Challenging Machine Learning Problems in Computational Biology: Time-Varyin...

Eric P. Xing

calendar icon Jan 15, 2009 7977 views

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54:06

Activized Learning: Transforming Passive to Active with Improved Label Complexit...

Steve Hanneke

calendar icon Jan 15, 2009 6707 views

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39:02

Object Recognition and Segmentation by Association

Tomasz Malisiewicz

calendar icon Jan 15, 2009 6301 views

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37:52

Rare Category Detection for Spatial Data

Jingrui He

calendar icon Jan 15, 2009 8339 views

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52:21

Inference Complexity as Learning Bias

Pedro Domingos

calendar icon Jan 15, 2009 4886 views

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22:31

Differentiable Sparse Coding

David Bradley

calendar icon Jan 15, 2009 6893 views

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49:48

Learning Patterns of the Brain: Machine Learning Challenges of fMRI Analysis

Mark Palatucci

calendar icon Oct 21, 2008 8539 views

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34:22

Exploiting document structure and feature hierarchy for semi-supervised domain a...

Andrew Arnold

calendar icon Oct 21, 2008 5350 views

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18:55

Feature Selection via Block-Regularized Regression

Seyoung Kim

calendar icon Oct 21, 2008 5712 views

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47:33

Probability Distributions on Permutations: Compact Representations and Inference

Jonathan Huang

calendar icon Apr 17, 2008 9920 views

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36:56

Discovering Cyclic Causal Models by Independent Components Analysis

Gustavo Lacerda

calendar icon Feb 27, 2008 6305 views

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47:26

Overview of New Developments in Boosting

Joseph K. Bradley

calendar icon Feb 21, 2008 8653 views

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54:21

Relational Learning as Collective Matrix Factorization

Ajit Singh

calendar icon Feb 14, 2008 10097 views

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53:39

Structured Prediction: Maximum Margin Techniques

Nathan Ratliff

calendar icon Feb 7, 2008 7991 views

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