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Page history last edited by Lester Mackey 9 years, 5 months ago

Berkeley Machine Learning Tea

 

Machine learning tea is a weekly informal gathering for 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 15-30 minute mini-talk on an interesting application, technique, dataset, or puzzle. Snacks and drinks will be served.

 

When: Weds. 3-4pm

Where: Soda Hall Fifth Floor Lounge 

Format:

    4:00pm - tea and cookies

    4:15pm - talk

    4:35pm - 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: The Real Time Informational Efficiency of Financial Markets [Reza Shabani]
  • Mar. 17: The Statistics of Natural Objects [Jon Barron]
  • Mar. 24: (Spring break)
  • Mar. 31: Open Forum
  • Apr. 7: Fast Sparse + Low-rank Matrix Decompositions [Lester Mackey
  • Apr. 14: One-shot Execution in Markov Decision Processes [Teodor Moldovan]
  • Apr. 21: Nonparametric combinatorial sequence models [Fabian Wauthier]
  • Apr. 28: Combinatorial Methods to Reduce State-Spaces for Hidden Markov Models [Bonnie Kirkpatrick]
  • May 5: Single Linkage Agglomerative Clustering with Linear Algebra [Ali Rahimi]

 

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 Fifth Floor Lounge is in Soda Hall, which is here. Go in the entrance on Le Roy Ave and proceed up the stairs on your left to the fifth floor.  You should see the door to the Fifth Floor Lounge adorned with a "Machine Learning Tea" sign.  If the door is closed, knock, and it shall be opened.

 

 

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