1. Big Learning: Algorithms, Systems, and Tools for Learning at Scale December 17-18, 2011 http://biglearn.org

2. The Organizing Committee (14) Primary Organizers Joseph Gonzalez (CMU) Sameer Singh (UMass Amherst) Alice Zheng (Microsoft Research) Graham Taylor (NYU) James Bergstra (Harvard) Misha Bilenko (Microsoft Research) Yucheng Low (CMU) Advisory Committee Sugato Basu (Google Research) Alexander J. Smola (Yahoo/NICTA) Michael Franklin (Berkeley) Andrew McCallum (UMass Amherst) Yoshua Bengio (UMontreal) Carlos Guestrin (CMU) Michael Jordan (Berkeley)

3. Program Committee (44) Frederic Bastien Sugato Basu Ron Bekkerman Kedar Bellare Danny Bickson Joseph Bradley Mihai Budiu Polo Chau Dan Ciresan Ronan Collobert Ofer Dekel Gregory Druck Khalid El-Arini Clement Farabet Amit Goyal Arthur Gretton Firas Hamze Matt Hoffman Michael Isard Paul Ivanov Alex Krizhevsky Aapo Kyrola Anthony Lee Frank Mcsherry Roland Memisevic Volodymyr Mnih Anders Mueller Jim Mutch Alexandre Passos Nicolas Pinto Rajat Raina Karl Schultz Hannes Schulz Alex Smola Balaji Vasan Srinivasan Vasily Volkov Markus Weimer Kilian Weinberger Michael Wick Jing Xiang Yisong Yue Matei Zaharia Matthew Zeiler Martin Zinkevich

4. History of Big Learning at NIPS 2010: Learning on Cores, Clusters and Clouds (LCCC) Workshop 2009: Large-Scale Machine Learning: Parallelism and Massive Datasets 2008: Parallel Implementations of Learning Algorithms 2007: Efficient Machine Learning - Overcoming Computational Bottlenecks in Machine Learning 2007: Learning Using Many Examples Tutorial by Andrew Moore and Alex Gray

5. “Scaling Up ML” Book Cambridge U. Press, shipping January 21 contributed chapters Platforms – map-reduce, multi-node/core, GPU, FPGA… Algorithms – Boosted trees, SVMs, DBNs, clustering… Tasks and applications – Supervised, semi/unsupervised, online, feature selection, learning and inference in graphical models – Text classification, vision, speech recognition, … Representative yet very sparse sample of the field

6. Workshop Motivation Big Learning Learning on Big Data Hardware- accelerated learning Fast inference in graphical models Model selection and significance testing Analysis of parallel learning algorithms Great diversity across tasks, platforms and algorithms Many common themes – Dataflow, distribution and coordination, speed-accuracy trade-offs, parallel/distributed convergence, …

7. Big Learning Workshop Friday morning: Hardware accelerated learning Friday afternoon: Applications and methodology Saturday morning:Systems & Tools Saturday afternoon: Models & Algorithms Tutorials: – Vowpal Wabbit Software: Today (2:00 – 3:30) – GraphLab Software: Tomorrow (2:00 – 3:30)

8. Please vote for your favorite talk(s): – http://biglearn.org/besttalk.html http://biglearn.org/besttalk.html Awesome mystery prize sponsored by NVIDIA! – What could it be? We’re not telling! Best Talk Award!

9. Schedule Changes Change in Afternoon Session: 5:255:55Miguel Araujo and Charles Parker Big Machine Learning made Easy 5:556:15Tammo Kruger Fast Cross-Validation via Sequential Analysis 6:156:45Poster Session 6:457:30Daniel Whiteson Machine Learning's Role in the Search for Fundamental Particles 7:307:50Ariel Kleiner Bootstrapping Big Data

10. Posters and Coffee Breaks Hang up your posters as early as possible – We will provide you with tape – Please do not hang posters on green walls Coffee station in Floor -1 (next to the Library)

11. Big Thanks to Our Sponsors