Carnegie Mellon Machine Learning Lunch seminar

Carnegie Mellon Machine Learning Lunch seminar

20 Lectures · 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

Related categories

Uploaded videos:

video-img
47:33

Probability Distributions on Permutations: Compact Representations and Inference

Jonathan Huang

Apr 17, 2008

 · 

9872 Views

Lecture
video-img
54:21

Relational Learning as Collective Matrix Factorization

Ajit Singh

Feb 14, 2008

 · 

10052 Views

Lecture
video-img
53:39

Structured Prediction: Maximum Margin Techniques

Nathan Ratliff

Feb 07, 2008

 · 

7955 Views

Lecture
video-img
36:56

Discovering Cyclic Causal Models by Independent Components Analysis

Gustavo Lacerda

Feb 27, 2008

 · 

6269 Views

Lecture
video-img
47:26

Overview of New Developments in Boosting

Joseph K. Bradley

Feb 21, 2008

 · 

8619 Views

Lecture
video-img
49:48

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

Mark Palatucci

Oct 21, 2008

 · 

8499 Views

Lecture
video-img
18:55

Feature Selection via Block-Regularized Regression

Seyoung Kim

Oct 21, 2008

 · 

5689 Views

Lecture
video-img
34:22

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

Andrew Arnold

Oct 21, 2008

 · 

5308 Views

Lecture
video-img
54:06

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

Steve Hanneke

Jan 15, 2009

 · 

6663 Views

Lecture
video-img
58:44

Probabilistic Decision-Making Under Model Uncertainty

Joelle Pineau

Jan 15, 2009

 · 

12094 Views

Lecture
video-img
39:02

Object Recognition and Segmentation by Association

Tomasz Malisiewicz

Jan 15, 2009

 · 

6256 Views

Lecture
video-img
26:24

Local Minima Free Parameterized Appearance Models

Minh Hoai Nguyen

Jan 15, 2009

 · 

6852 Views

Lecture
video-img
52:21

Inference Complexity as Learning Bias

Pedro Domingos

Jan 15, 2009

 · 

4849 Views

Lecture
video-img
22:31

Differentiable Sparse Coding

David Bradley

Jan 15, 2009

 · 

6862 Views

Lecture
video-img
27:03

Partially Observed Maximum Entropy Discrimination Markov Networks

Jun Zhu

Jan 15, 2009

 · 

5639 Views

Lecture
video-img
37:52

Rare Category Detection for Spatial Data

Jingrui He

Jan 15, 2009

 · 

8312 Views

Lecture
video-img
01:05:50

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

Eric P. Xing

Jan 15, 2009

 · 

7946 Views

Lecture
video-img
24:31

Weighted Graphs and Disconnected Components: Patterns and a Generator

Mary McGlohon

Mar 29, 2009

 · 

5390 Views

Lecture
video-img
01:00:27

Large Scale Scene Matching for Graphics and Vision

James Hays

Mar 29, 2009

 · 

6254 Views

Lecture
25:42

Efficient Parallel Learning of Linear Dynamical Systems on SMPs

Lei Li

Mar 29, 2009

 · 

4527 Views

Lecture