The Fifth Elephant 2017
On data engineering and application of ML in diverse domains
Jul 2017
24 Mon
25 Tue
26 Wed
27 Thu 08:15 AM – 10:00 PM IST
28 Fri 08:15 AM – 06:25 PM IST
29 Sat
30 Sun
##Theme and format
The Fifth Elephant 2017 is a four-track conference on:
The Fifth Elephant is a conference for practitioners, by practitioners.
Talk submissions are now closed.
You must submit the following details along with your proposal, or within 10 days of submission:
##About the conference
This year is the sixth edition of The Fifth Elephant. The conference is a renowned gathering of data scientists, programmers, analysts, researchers, and technologists working in the areas of data mining, analytics, machine learning and deep learning from different domains.
We invite proposals for the following sessions, with a clear focus on the big picture and insights that participants can apply in their work:
##Selection Process
We will notify you if we move your proposal to the next round or reject it. A speaker is NOT confirmed for a slot unless we explicitly mention so in an email or over any other medium of communication.
Selected speakers must participate in one or 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.
##Travel grants
Partial or full grants, covering travel and accomodation are made available to speakers delivering full sessions (40 minutes) and workshops. Grants are limited, and are given in the order of preference to students, women, persons of non-binary genders, and speakers from Asia and Africa.
##Commitment to Open Source
We believe in open source as the binding force of our community. If you are describing a codebase for developers to work with, we’d like for 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), you should consider picking up a sponsorship. We recognise that there are valid reasons for commercial licensing, but ask that you support the conference in return for giving you an audience. Your session will be marked on the schedule as a “sponsored session”.
##Important Dates:
##Contact
For more information about speaking proposals, tickets and sponsorships, contact info@hasgeek.com or call +91-7676332020.
Hosted by
Lakshman Prasad
@becomingguru
Submitted May 31, 2017
There is a study that gave the same data set to many teams competent to analyse it and asked them all the same question: “whether soccer referees are more likely to give red cards to dark skin toned players than light skin toned players”: http://home.uchicago.edu/~npope/crowdsourcing_paper.pdf
The plurality of the analytical metods used, the distributions used to fit the data and make the predictions by these participating 61 analysts, in 29 teams is very interesting.
We explore the paper, the data set, the models and go through the rationale, why each of the modelling choice may have made sense and compare how the 29 results compare.
The paper makes a case why crowdsourcing the data reasearch and then collaborating to reach a conclusion makes sense to avoid any inherent biases and modelling errors.
Lakshman Prasad has been interested in data analytics and data science for a long time.
He was very fascinated with this UChicago paper that explicitly gets the results for the same data set and compares the approaches.
He currently works for a management consulting firm to develop technology solutions, that may involve data analytics.
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