Machine Learning, Distributed and Parallel Computing, and High-performance Computing are the themes for this year’s edition of Fifth Elephant.
The deadline for submitting a proposal is 15th June 2015
We are looking for talks and workshops from academics and practitioners who are in the business of making sense of data, big and small.
This track is about general, novel, fundamental, and advanced techniques for making sense of data and driving decisions from data. This could encompass applications of the following ML paradigms:
- Statistical Visualizations
- Unsupervised Learning
- Supervised Learning
- Semi-Supervised Learning
- Active Learning
- Reinforcement Learning
- Monte-carlo techniques and probabilistic programming
- Deep Learning
Across various data modalities including multi-variate, text, speech, time series, images, video, transactions, etc.
This track is about tools and processes for collecting, indexing, and processing vast amounts of data. The theme includes:
- Distributed and Parallel Computing
- Real Time Analytics and Stream Processing
- MapReduce and Graph Computing frameworks
- Kafka, Spark, Hadoop, MPI
- Stories of parallelizing sequential programs
- Cost/Security/Disaster Management of Data
HasGeek believes in open source as the binding force of our community. If you are describing a codebase for developers to work with, we’d like it to be available under a permissive open source license. If your software is commercially licensed or available under a combination of commercial and restrictive open source licenses (such as the various forms of the GPL), please consider picking up a sponsorship. We recognize that there are valid reasons for commercial licensing, but ask that you support us in return for giving you an audience. Your session will be marked on the schedule as a sponsored session.
If you are interested in conducting a hands-on session on any of the topics falling under the themes of the two tracks described above, please submit a proposal under the workshops section. We also need you to tell us about your past experience in teaching and/or conducting workshops.
High Performance Computing in R
This is a hands-on workshop focused on the high performance aspects of R programming. The attendees would get to learn how to identify the performance issues and address them through the use of various R packages. This workshop is targeted towards audience with a basic familiarity in R.
The following is the outline of the proposed workshop.
1.Understanding R’s slowness
2.Identifying bottlenecks through Profiling
3.Efficient coding practices for improving speed
4.Achieving speed through compiled codes (C++)
5.Overcoming memory limitations
6.Parallel Processing in R
The workshop will include a live demo on each of these topics followed by hands-on lab for the participants.
A basic level of programming familiarity with R.
The technology requirements along with a list of R packages to be installed would be provided a week before the workshop. The R scripts and the datasets would be uploaded to Github and shared with the participants.
Ravishankar Rajagopalan is Senior Principal Data Engineer in the Data Science Infrastructure (DSI) team at 7 Innovation Lab’s Data Sciences Group (DSG). As part of DSI, Ravi focusses on developing scalable analytics products. Prior to 7, he worked with GE Power and Water as part of advanced analytics team and with Mu Sigma.
Ravi has been using R for 10+ years and has conducted R trainings at 7, GE and Mu Sigma. He has also built analytics products through the use of R. He taught undergraduate/graduate level courses during his Ph.D. Ravi holds a Ph.D. in Applied Statistics from The Ohio State University.