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.
Track 1: Discovering Insights and Driving Decisions
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.
Track 2: Speed at Scale
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
Commitment to Open Source
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.
On building a cloud-based black-box predictive modeling system
Data Analytics platforms, with predictive models at their core, are the buzzword in Enterprise Analytics. Having been on both sides - a consultant providing analytics and a consumer of analytics, I’ve realized that there are few, if any, runaway winners. Rightly so. It is one of the hottest growth areas. This talk would go over some of the ingredients to building a successful data analytics platform
UI/UX is definitely a centerpiece to a successful analytics platform. But what needs to get in there? (There is no single correct answer to this) What are the data science components? What is the impact on the design/architecture of the data science components when data scales?
It is easy to build a machine learning model. But what does it take to build a state-of-art, or even a reasonably good, model ? Is there a secret sauce? When data scales, what are the trade-offs to consider? How far can one go when expert domain knowledge is not available in-house ?
The talk would try to answer those above questions, along with the constraints various choices impose when creating the platform.
On the modeling front, there will be emphasis on the following: Feature engineering, modeling selection, emsembling, importance of bias-variance and generalization.
An interest in Analytics.
Just to draw some distinction between full-fledged apps(Eg: e-commerce apps) and data platforms : Data platforms are primarily meant for data analysts/data scientists/business owners to recommend/make better decisions. Mostly, they solve just one business problem(and well!). The goal is to plug-and-play the enterprise data into them and get insights/recommendations. Some other jargons for them include: APIs for machine intelligence, Productizing Analytics
Bargava Subramanian is a Senior Statistician(Data Scientist) at Cisco Systems, India. He has a Masters from University of Maryland, College Park, USA.
- A crash course in Classification Algorithms (https://fifthelephant.talkfunnel.com/2014/1158-machine-learning-using-r-crash-course-in-classific) - Workshop, Fifth Elephant 2014
- Mind Map for the talk: https://atlas.mindmup.com/2015/06/f25ade70fb4e0132aa0b1a2e2c3822b9/data_platform/index.html