Jul 2016
25 Mon
26 Tue
27 Wed
28 Thu 08:30 AM – 06:25 PM IST
29 Fri 08:30 AM – 06:15 PM IST
30 Sat 08:45 AM – 05:00 PM IST
31 Sun 08:15 AM – 06:00 PM IST
Jul 2016
25 Mon
26 Tue
27 Wed
28 Thu 08:30 AM – 06:25 PM IST
29 Fri 08:30 AM – 06:15 PM IST
30 Sat 08:45 AM – 05:00 PM IST
31 Sun 08:15 AM – 06:00 PM IST
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:
The deadline for submitting proposals is 30th April 2016
This year’s edition spans two days of hands-on workshops and conference. We are inviting proposals for:
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).
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.
##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
Ashish Kulkarni
@kulashish
Submitted Jun 15, 2016
Fashion e-commerce industries experience a lot of product returns (or exchange) from customers. Most of these are attributed to incorrect size (or fitment). The talk will focus on this problem and present a solution to reduce such returns. Specifically, we present a data science driven approach to profile our customers based on their past purchases and returns and use that to recommend the right size product or flag a potential return.
Refer to the attached presentation deck.
Motivation
Problem definition
Our approach
Evaluation
Conclusion
N/A
Ashish Kulkarni works as a Principal data scientist at Jabong Labs. He’s currently also a Ph.D. candidate in the Computer Science department at IIT Bombay. His research interest is in the area of interactive machine learning and its application to information extraction and retrieval. Practical applications like information extraction, retrieval, machine translation, to name a few, might benefit from autonomous machine learning models, aided by user preferences. Interactive machine learning opens up promising avenues while posing research challenges in designing appropriate tools and algorithms. Studying and addressing these challenges forms the primary focus of his research. He has published in reputed conferences including IJCAI, PAKDD, K-CAP and others. Ashish has over eight years of prior industry experience.
Jul 2016
25 Mon
26 Tue
27 Wed
28 Thu 08:30 AM – 06:25 PM IST
29 Fri 08:30 AM – 06:15 PM IST
30 Sat 08:45 AM – 05:00 PM IST
31 Sun 08:15 AM – 06:00 PM IST
Hosted by
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