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
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
Santosh GSK
@santoshgadde
Submitted Apr 30, 2017
The art of trading between exploiting the best arm versus exploring for further knowledge of other arms has long been studied as Bandit Algorithms in various fields of clinical trials, designing financial portfolios, etc. Recently, in website optimization, these algorithms have been used for optimizing click through rates and performing A/B testing. However, these algorithms has the potential to be applied at several other contexts where we need to optimize for reward by exploring possible arms.
In this talk, I would be presenting an approach based on Contextual Multi-armed Bandit algorithms for achieving a better tradeoff between user’s expectation of faster replies and doctor’s burnout rate on a QnA platform like Practo Consult.
Practo’s Consult platform helps users get their health queries clarified by professional qualified doctors. As we value both users and doctors as our customers, we optimize for enhancing their experience while using Consult. This would entail good quality and faster replies for user’s health queries, whereas doctors expect good quality questions and number of assignments to be correlating with their answering capacity. This is an interesting problem because optimizing for one would compromise the other. For example, if we assign all questions to only the high performing doctors, the remaining non-performing doctors cannot undo their behavior as they won’t get enough questions. Whereas, if we balance the assignment of questions among doctors, it won’t be optimizing for faster replies. Ideally, we want to achieve a tradeoff between the two.
Santosh GSK is working as a Senior Data Scientist at Practo. He has 5 years of industry experience in Data Science and 3 years as a ML Researcher with half a dozen publications in leading conferences. He is currently working on building data-driven solutions to improve both patient and doctor experience at Practo. Prior to that, he was working as a Data Scientist at Housing.com, where he worked on lead prediction and property price prediction models.
https://drive.google.com/open?id=0B_FveDU9pdasdjhpVDZpMXRKNFk
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
Hosted by
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