The Fifth Elephant is India’s most renowned data science conference. It is a space for discussing some of the most cutting edge developments in the fields of machine learning, data science and technology that powers data collection and analysis.
Machine Learning, Distributed and Parallel Computing, and High-performance Computing continue to be the themes for this year’s edition of Fifth Elephant.
We are now accepting submissions for our next edition which will take place in Bangalore 28-29 July 2016.
#Tracks
We are looking for application level and tool-centric talks and tutorials on the following topics:
- Deep Learning
- Text Mining
- Computer Vision
- Social Network Analysis
- Large-scale Machine Learning (ML)
- Internet of Things (IoT)
- Computational Biology
- ML in healthcare
- ML in education
- ML in energy and ecology
- ML in agriculrure
- Analytics for emerging markets
- ML in e-governance
- ML in smart cities
- ML in defense
The deadline for submitting proposals is 30th April 2016
Format
This year’s edition spans two days of hands-on workshops and conference. We are inviting proposals for:
- Full-length 40 minute talks.
- Crisp 15-minute talks.
- Sponsored sessions, 15 minute duration (limited slots available; subject to editorial scrutiny and approval).
- Hands-on Workshop sessions, 3 and 6 hour duration.
Selection process
Proposals will be filtered and shortlisted by an Editorial Panel. We urge you to add links to videos / slide decks when submitting proposals. This will help us understand your past speaking experience. Blurbs or blog posts covering the relevance of a particular problem statement and how it is tackled will help the Editorial Panel better judge your proposals.
We expect you to submit an outline of your proposed talk – either in the form of a mind map or a text document or draft slides within two weeks of submitting your proposal.
We will notify you about the status of your proposal within three weeks of submission.
Selected speakers must participate in one-two rounds of rehearsals before the conference. This is mandatory and helps you to prepare well for the conference.
There is only one speaker per session. Entry is free for selected speakers. As our budget is limited, we will prefer speakers from locations closer home, but will do our best to cover for anyone exceptional. HasGeek will provide a grant to cover part of your travel and accommodation in Bangalore. Grants are limited and made available to speakers delivering full sessions (40 minutes or longer).
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 licence. If your software is commercially licensed or available under a combination of commercial and restrictive open source licences (such as the various forms of the GPL), please consider picking up a sponsorship. We recognise 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.
Key dates and deadlines
- Revised paper submission deadline: 17 June 2016
- Confirmed talks announcement (in batches): 13 June 2016
- Schedule announcement: 30 June 2016
- Conference dates: 28-29 July 2016
##Venue
The Fifth Elephant will be held at the NIMHANS Convention Centre, Dairy Circle, Bangalore.
##Contact
For more information about speaking proposals, tickets and sponsorships, contact info@hasgeek.com or call +91-7676332020.
Hosted by
The Fifth Elephant - known as one of the best data science and Machine Learning conference in Asia - has transitioned into a year-round forum for conversations about data and ML engineering; data science in production; data security and privacy practices. more
SD
Soumen Dey
@soumendey
Hierarchical Bayes Approach and Implementation of MCMC in an Ecological Study
Submitted Jul 18, 2016
Section:
Full talk
Technical level:
Advanced
The Bayesian paradigm for analysing data has gained unmatched popularity at most of the fields of statistical application in the late twentieth century. Bayesian methods permits one to construct statistical models by simultaneously using the current data and all the prior information on hand to make inference about the unknown nature of the underlying process, in a marvellously simple way. But the real reason for the popularity of Bayesian methods is the ability to solve real world data related problems by using the hierarchical structure and Markov Chain Monte Carlo (MCMC). An enormous number of problems that were deemed to be computational nightmares now cracked like open eggs by the rebirth of MCMC. We will show some examples to show the usefulness of MCMC and how much cautious the experimenter should be before expecting MAGIC!
Outline
- The talk begins by stating a research problem in ecology. The characteristics of the problem will be explained with the types of inferences we are interested in. I will also show the unavailability of any closed form solutions for them.
- This will be followed by a brief introduction to Bayesian methods of analysis and by Bayesian approach of learning from data. I will explain how hierarchical structure helps in modelling data. Next will be a description of a generic set of Markov Chain Monte Carlo algorithms that is frequently used to fit the hierarchical models.
- Then I will go back to the problem described at the beginning of the talk and use hierarchical Bayesian approach with MCMC to solve it. I will explain usefulness and some drawbacks of MCMC under Bayesian approach. The talk will end after a discussion on generality and robustness of Bayesian paradigm.
##Popular Quotes
- `Inside every nonBayesian there is a Bayesian struggling to get out.’ - Dennis V. Lindley
- `The practising Bayesian is well advised to become friends with as many numerical analysts as possible.’ - James O. Berger
Requirements
- Familiarity with the Bayesian and frequentist approach of inference
- Familiarity with the analytical evaluation of a joint, conditional and marginal density function
Speaker bio
Soumen Dey is currently a research scholar in Indian Statistical Institute, Bangalore. He has about 4 years of experience in handling ecological data and building statistical models. His research interests include model selection, modelling of data from multiple sources, Bayesian statistics.
Slides
https://drive.google.com/file/d/0B3Mnscd1GGfkNEw1RTNHODJqRlE/view?usp=sharing
Comments
Hosted by
The Fifth Elephant - known as one of the best data science and Machine Learning conference in Asia - has transitioned into a year-round forum for conversations about data and ML engineering; data science in production; data security and privacy practices. more
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