Berkeley Machine Learning Tea
Machine learning tea is a weekly informal gathering for both statistics and machine learning researchers and those working in systems, AI, natural language, vision, computational biology, etc., hoping to apply statistical techniques. At the gathering, we will be entertained by a 10-15 minute mini-talk on an interesting application, technique, dataset, or puzzle. Snacks and drinks will be served.
When: Thurs. 4-5pm
Where: RAD Lab Lounge (Soda 465)
Format:
4:00pm - tea and cookies
4:15pm - talk
4:30pm - questions + discussion
Please volunteer to give a tea talk! Contact the tea masters at (tea-organizers AT lists.eecs.berkeley.edu) if you're interested.
Subscribe to tea AT lists.eecs.berkeley.edu to get email announcements about the tea.
Why tea? We are inspired by the grand traditions at Gatsby, Toronto, and MIT.
Spring 2011 Schedule
- Jan. 28: Large Scale Image Annotations on Amazon Mechanical Turk [Subhransu Maji]
- Feb. 3: Document Interpolation: A Nearest Neighbor Approach [Brian Gawalt]
- Feb. 10: Estimating the unseen: optimal estimators for entropy, support size, and other such properties [Greg Valiant]
- Feb. 17: Using AI to Find Free Food [Greg Woloschyn]
- Feb. 24: The Case for ML in Understanding,Verifying, and Optimizing Programs [Mayur Naik]
- Mar. 3: Home Networking: The Crowd vs. the Cloud [Christophe Diot]
- Mar. 10:
- Mar. 17:
- Mar. 24: (Spring break)
- Mar. 31:
- Apr. 7:
- Apr. 14:
- Apr. 21:
- Apr. 28:
- May 5:
Spring 2010 Schedule
Fall 2009 Schedule
Spring 2009 Schedule
- Jan. 30: Classifiers for Real Time Object Detection [Subhransu Maji]
- Feb. 6: Finding Solace in High Dimensions [Ariel Kleiner]
- Feb. 13: Methodology for Evaluating Models
- Feb. 20: Open forum
- Feb. 27: Deciphering Honey Bee Dances and Stock Market Swings [Emily Fox]
- Mar. 6: Hadoop for Berkeley Machine Learners [Owen O'Malley]
- Mar. 13: Hands-on with Hadoop [Andy Konwinski and Matei Zaharia]
- Mar. 20: (cancelled - concurrent talk by Andrew Gelman)
- Mar. 27: (spring break)
- Apr. 3: Probabilistic Matrix Factorization with Gaussian Processes [Raquel Urtasun]
- Apr. 10: Debate: Is learning theory useful for practioners? [Alekh Agarwal versus Aria Haghighi]
- Apr. 17: Oceanic Park: Reconstructing Extinct Protolanguages using Machine Learning [Alexandre Bouchard]
- Apr. 24: From Books to Blenders: Learning under Different Training and Test Distributions [John Blitzer]
- May 1: Understanding Human Genetic Variation: Two Statistical Problems [Sriram Sankararaman]
- May 8: BayesStore: Supporting Statistical Models in Probabilistic Databases [Daisy Wang]
Fall 2008 Schedule
How do I get to machine learning tea?
The RadLab is in Soda Hall, which is here. Go in the entrance on Le Roy Ave and go immediately left. You should see the door to the RadLab. If it is locked, ring the door bell and someone will come get you. After you've gone in, walk pass the conference rooms, and the lounge should be on the left (there might be a curtain).
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